Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images
Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address road damage detection issues. First, to the best of our knowledge, for the first time, a large‐scale road damage data set is prepared, comprisi...
Saved in:
| Published in | Computer-aided civil and infrastructure engineering Vol. 33; no. 12; pp. 1127 - 1141 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc
01.12.2018
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1093-9687 1467-8667 |
| DOI | 10.1111/mice.12387 |
Cover
| Abstract | Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address road damage detection issues. First, to the best of our knowledge, for the first time, a large‐scale road damage data set is prepared, comprising 9,053 road damage images captured using a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. Next, we used state‐of‐the‐art object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage data set, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/). |
|---|---|
| AbstractList | Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address road damage detection issues. First, to the best of our knowledge, for the first time, a large‐scale road damage data set is prepared, comprising 9,053 road damage images captured using a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. Next, we used state‐of‐the‐art object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage data set, our experimental results, and the developed smartphone application used in this study are publicly available (https://github.com/sekilab/RoadDamageDetector/). Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address road damage detection issues. First, to the best of our knowledge, for the first time, a large‐scale road damage data set is prepared, comprising 9,053 road damage images captured using a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. Next, we used state‐of‐the‐art object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage data set, our experimental results, and the developed smartphone application used in this study are publicly available ( https://github.com/sekilab/RoadDamageDetector/ ). |
| Author | Seto, Toshikazu Omata, Hiroshi Kashiyama, Takehiro Sekimoto, Yoshihide Maeda, Hiroya |
| Author_xml | – sequence: 1 givenname: Hiroya surname: Maeda fullname: Maeda, Hiroya organization: The University of Tokyo – sequence: 2 givenname: Yoshihide surname: Sekimoto fullname: Sekimoto, Yoshihide email: sekimoto@iis.u-tokyo.ac.jp organization: The University of Tokyo – sequence: 3 givenname: Toshikazu surname: Seto fullname: Seto, Toshikazu organization: The University of Tokyo – sequence: 4 givenname: Takehiro surname: Kashiyama fullname: Kashiyama, Takehiro organization: The University of Tokyo – sequence: 5 givenname: Hiroshi surname: Omata fullname: Omata, Hiroshi organization: The University of Tokyo |
| BookMark | eNp9kMFLwzAUxoNMcE4v_gUBb0Jn06RNe5Ru6mAqqLt4CWn6umV27Uwyxv57s9aTiO_yHo_f973Hd44GTdsAQlckHBNftxutYEwimvITNCQs4UGaJHzg5zCjQZak_AydW7sOfTFGh-jjtZUlnsiNXAKegAPldNtg2ZQ4r6W1utJKdquF1c3SI7DFz7AzsvbN7VvzafFeuxV-20jjtiv_D54d3ewFOq1kbeHyp4_Q4n76nj8G85eHWX43DxSNGQ84D9MokxQqlUYVBRLFhNBM0SyGMGZlpZIiIUmRRqVkkhaMlTFJMwpM8rigjI7Qde-7Ne3XDqwT63ZnGn9SRIR6NGIdddNTyrTWGqjE1mj_8kGQUByzE8fsRJedh8NfsNKui8EZqeu_JaSX7HUNh3_MxdMsn_aab7jKgqc |
| CitedBy_id | crossref_primary_10_1109_TITS_2024_3360725 crossref_primary_10_3390_app14114424 crossref_primary_10_1016_j_aei_2023_101940 crossref_primary_10_1109_TIV_2022_3204583 crossref_primary_10_3390_drones8110692 crossref_primary_10_1109_JSEN_2021_3135388 crossref_primary_10_1016_j_aej_2024_11_081 crossref_primary_10_1016_j_autcon_2021_103634 crossref_primary_10_3390_s20195564 crossref_primary_10_1109_TITS_2022_3208188 crossref_primary_10_3390_buildings12081225 crossref_primary_10_1007_s13349_022_00643_8 crossref_primary_10_3390_s22228878 crossref_primary_10_1007_s42947_020_0098_9 crossref_primary_10_1016_j_autcon_2021_103991 crossref_primary_10_1109_ACCESS_2024_3481649 crossref_primary_10_1098_rsta_2022_0172 crossref_primary_10_1016_j_ymssp_2021_108377 crossref_primary_10_1111_exsy_13784 crossref_primary_10_3390_app13031999 crossref_primary_10_1016_j_engfailanal_2023_107237 crossref_primary_10_1117_1_JEI_31_4_043011 crossref_primary_10_3390_ma13132960 crossref_primary_10_1016_j_engfracmech_2022_108467 crossref_primary_10_3390_s23010053 crossref_primary_10_1016_j_autcon_2024_105682 crossref_primary_10_2166_hydro_2022_132 crossref_primary_10_1016_j_aei_2024_103036 crossref_primary_10_1155_2021_3511375 crossref_primary_10_1109_ACCESS_2020_2998427 crossref_primary_10_1080_10298436_2023_2183401 crossref_primary_10_1109_ACCESS_2025_3532832 crossref_primary_10_1016_j_engappai_2023_106359 crossref_primary_10_1109_TITS_2023_3327494 crossref_primary_10_3390_su16052207 crossref_primary_10_1007_s44150_022_00060_x crossref_primary_10_3390_app12115320 crossref_primary_10_1016_j_autcon_2021_103973 crossref_primary_10_3934_math_2019_5_1320 crossref_primary_10_1007_s10514_020_09964_3 crossref_primary_10_4018_IJCINI_356363 crossref_primary_10_1016_j_autcon_2021_103606 crossref_primary_10_1016_j_conbuildmat_2024_139026 crossref_primary_10_3390_su15086610 crossref_primary_10_1061__ASCE_CP_1943_5487_0000883 crossref_primary_10_1051_matecconf_202439302005 crossref_primary_10_1016_j_autcon_2023_105186 crossref_primary_10_1109_TITS_2024_3382837 crossref_primary_10_1016_j_jobe_2023_107961 crossref_primary_10_1016_j_autcon_2023_105062 crossref_primary_10_1111_exsy_12647 crossref_primary_10_1007_s11803_022_2074_7 crossref_primary_10_1111_mice_13128 crossref_primary_10_1007_s42421_022_00056_5 crossref_primary_10_1038_s41598_023_50671_7 crossref_primary_10_3390_s20102778 crossref_primary_10_1007_s11668_022_01430_9 crossref_primary_10_3390_s24051467 crossref_primary_10_1016_j_aei_2020_101182 crossref_primary_10_3390_buildings12112019 crossref_primary_10_3390_su142316189 crossref_primary_10_1155_2021_6654723 crossref_primary_10_2139_ssrn_4353622 crossref_primary_10_1109_ACCESS_2020_2991968 crossref_primary_10_1111_mice_13132 crossref_primary_10_1111_mice_13010 crossref_primary_10_58922_transportes_v32i2_2796 crossref_primary_10_1007_s12145_022_00871_y crossref_primary_10_1016_j_engappai_2023_106575 crossref_primary_10_1088_1742_6596_1903_1_012008 crossref_primary_10_32604_cmc_2022_029544 crossref_primary_10_1093_comjnl_bxac029 crossref_primary_10_1109_JPROC_2022_3153408 crossref_primary_10_1016_j_conbuildmat_2023_134212 crossref_primary_10_3390_s22093537 crossref_primary_10_1016_j_autcon_2024_105481 crossref_primary_10_1002_gdj3_260 crossref_primary_10_1061_JPCFEV_CFENG_4671 crossref_primary_10_1111_mice_13224 crossref_primary_10_1016_j_autcon_2020_103336 crossref_primary_10_1061_JPEODX_PVENG_1194 crossref_primary_10_1016_j_conbuildmat_2023_133593 crossref_primary_10_1007_s42452_024_06129_0 crossref_primary_10_1007_s13349_020_00386_4 crossref_primary_10_1016_j_autcon_2024_105375 crossref_primary_10_1111_mice_13233 crossref_primary_10_18287_2412_6179_CO_844 crossref_primary_10_3390_eng5040177 crossref_primary_10_3390_s21030689 crossref_primary_10_1007_s11042_025_20729_x crossref_primary_10_1177_03611981211012001 crossref_primary_10_1016_j_autcon_2021_103892 crossref_primary_10_1177_1475921719896813 crossref_primary_10_1016_j_autcon_2020_103230 crossref_primary_10_1111_mice_13358 crossref_primary_10_1016_j_autcon_2020_103477 crossref_primary_10_1080_10298436_2023_2247135 crossref_primary_10_3390_s20247071 crossref_primary_10_1515_jisys_2023_0147 crossref_primary_10_1016_j_prostr_2024_09_025 crossref_primary_10_1016_j_conbuildmat_2022_127968 crossref_primary_10_1002_tee_23672 crossref_primary_10_1109_ACCESS_2024_3517632 crossref_primary_10_3390_app14114705 crossref_primary_10_12815_kits_2020_19_2_89 crossref_primary_10_3390_su15086438 crossref_primary_10_1139_cjce_2021_0116 crossref_primary_10_1061__ASCE_CP_1943_5487_0000918 crossref_primary_10_1109_ACCESS_2021_3074019 crossref_primary_10_1007_s13369_024_09388_6 crossref_primary_10_1080_1206212X_2020_1758877 crossref_primary_10_1109_ACCESS_2024_3451708 crossref_primary_10_26634_jip_8_3_18451 crossref_primary_10_3390_app12073337 crossref_primary_10_1016_j_jmapro_2022_05_038 crossref_primary_10_1111_mice_12909 crossref_primary_10_1016_j_talanta_2022_123862 crossref_primary_10_18359_rcin_4385 crossref_primary_10_1002_stc_2766 crossref_primary_10_1061__ASCE_ST_1943_541X_0003140 crossref_primary_10_3390_app11020813 crossref_primary_10_1016_j_measurement_2023_113269 crossref_primary_10_1080_14942119_2024_2373009 crossref_primary_10_1002_stc_2764 crossref_primary_10_3390_asi7010011 crossref_primary_10_1016_j_engfailanal_2022_106714 crossref_primary_10_1109_TITS_2023_3287349 crossref_primary_10_1177_03611981211004973 crossref_primary_10_1016_j_measurement_2021_110641 crossref_primary_10_1109_TIE_2019_2945265 crossref_primary_10_3390_app112311193 crossref_primary_10_1016_j_conbuildmat_2025_140247 crossref_primary_10_1016_j_measurement_2024_115393 crossref_primary_10_1007_s10530_020_02434_y crossref_primary_10_3390_app122211529 crossref_primary_10_1155_2022_4684669 crossref_primary_10_1002_stc_2751 crossref_primary_10_14710_teknik_v44i1_51908 crossref_primary_10_1016_j_future_2019_05_028 crossref_primary_10_1061_JPEODX_0000373 crossref_primary_10_3390_bdcc8100136 crossref_primary_10_1016_j_eswa_2025_126581 crossref_primary_10_3233_JIFS_239289 crossref_primary_10_1016_j_jtte_2021_04_008 crossref_primary_10_11648_j_eas_20240904_13 crossref_primary_10_1016_j_autcon_2024_105297 crossref_primary_10_3390_s23198241 crossref_primary_10_1002_stc_2742 crossref_primary_10_3390_a16090452 crossref_primary_10_3390_app11073152 crossref_primary_10_1177_14759217221147015 crossref_primary_10_2208_jscejpe_77_1_28 crossref_primary_10_3389_fbuil_2022_1007886 crossref_primary_10_1016_j_engappai_2023_106880 crossref_primary_10_1016_j_conbuildmat_2020_119397 crossref_primary_10_3390_asi4040094 crossref_primary_10_1177_09544097231159707 crossref_primary_10_1016_j_measurement_2024_115119 crossref_primary_10_1155_2021_5589688 crossref_primary_10_1109_JIOT_2024_3385994 crossref_primary_10_7409_rabdim_023_017 crossref_primary_10_1109_ACCESS_2022_3196660 crossref_primary_10_1016_j_jweia_2020_104138 crossref_primary_10_1007_s42452_024_06207_3 crossref_primary_10_3390_infrastructures9100186 crossref_primary_10_1155_2022_4489770 crossref_primary_10_1016_j_measurement_2024_116453 crossref_primary_10_3390_rs14164037 crossref_primary_10_1061__ASCE_MT_1943_5533_0004605 crossref_primary_10_2174_1872212114999200914113515 crossref_primary_10_1016_j_jtte_2021_10_001 crossref_primary_10_1111_mice_13097 crossref_primary_10_3390_s24237724 crossref_primary_10_1016_j_autcon_2020_103176 crossref_primary_10_1111_exsy_12494 crossref_primary_10_3390_vehicles5030051 crossref_primary_10_1016_j_autcon_2022_104544 crossref_primary_10_3390_s21248406 crossref_primary_10_1016_j_autcon_2022_104664 crossref_primary_10_1142_S179396232150046X crossref_primary_10_1016_j_aei_2019_100937 crossref_primary_10_1109_ACCESS_2019_2961375 crossref_primary_10_3390_ijgi9030161 crossref_primary_10_1111_mice_13186 crossref_primary_10_1061_JPEODX_PVENG_1359 crossref_primary_10_26599_JIC_2023_9180032 crossref_primary_10_1177_1475921720985437 crossref_primary_10_1016_j_autcon_2020_103185 crossref_primary_10_1177_14759217221078766 crossref_primary_10_3389_fmats_2022_1058407 crossref_primary_10_1016_j_jtte_2021_03_005 crossref_primary_10_1016_j_autcon_2023_104945 crossref_primary_10_1088_1361_6501_ac8e22 crossref_primary_10_1134_S1064226920120049 crossref_primary_10_1007_s11554_024_01545_2 crossref_primary_10_1109_TITS_2023_3266776 crossref_primary_10_1109_TIV_2022_3182218 crossref_primary_10_3390_s21082595 crossref_primary_10_1016_j_dib_2024_110131 crossref_primary_10_1007_s00521_021_06279_x crossref_primary_10_1016_j_eswa_2024_123940 crossref_primary_10_1080_14942119_2023_2290795 crossref_primary_10_3390_s20226425 crossref_primary_10_3390_s22093471 crossref_primary_10_32604_cmc_2021_014170 crossref_primary_10_1016_j_jtte_2022_11_003 crossref_primary_10_1177_1475921720938486 crossref_primary_10_1142_S0129065720500409 crossref_primary_10_1177_03611981241239958 crossref_primary_10_1111_mice_13071 crossref_primary_10_1520_JTE20220298 crossref_primary_10_1109_JLT_2022_3209499 crossref_primary_10_1111_mice_13070 crossref_primary_10_3390_su15031866 crossref_primary_10_1007_s11042_024_19175_y crossref_primary_10_1016_j_physa_2019_123510 crossref_primary_10_2139_ssrn_4105324 crossref_primary_10_1016_j_conbuildmat_2022_129226 crossref_primary_10_1016_j_autcon_2022_104332 crossref_primary_10_1080_01431161_2024_2365813 crossref_primary_10_1016_j_ymssp_2024_111813 crossref_primary_10_1007_s00500_023_09103_x crossref_primary_10_1111_mice_12626 crossref_primary_10_1061_JPEODX_0000317 crossref_primary_10_1007_s13042_022_01555_1 crossref_primary_10_1177_0361198120965170 crossref_primary_10_1007_s41315_020_00141_4 crossref_primary_10_1016_j_autcon_2022_104344 crossref_primary_10_1177_03611981211057532 crossref_primary_10_1111_mice_12640 crossref_primary_10_1080_10298436_2023_2268796 crossref_primary_10_1109_ACCESS_2020_2981561 crossref_primary_10_3313_jls_56_255 crossref_primary_10_1139_cjce_2020_0246 crossref_primary_10_1016_j_tust_2020_103677 crossref_primary_10_1111_mice_12519 crossref_primary_10_1109_TITS_2023_3267433 crossref_primary_10_1016_j_rineng_2025_104546 crossref_primary_10_1016_j_mlwa_2024_100547 crossref_primary_10_1007_s41062_023_01308_1 crossref_primary_10_1109_TIV_2023_3326136 crossref_primary_10_1111_mice_12967 crossref_primary_10_1016_j_compbiomed_2020_103980 crossref_primary_10_1109_ACCESS_2022_3190014 crossref_primary_10_1177_1748006X20965111 crossref_primary_10_3390_jsan11010015 crossref_primary_10_1016_j_culher_2024_01_005 crossref_primary_10_1111_mice_12503 crossref_primary_10_1080_02827581_2022_2147213 crossref_primary_10_1111_mice_12741 crossref_primary_10_1111_mice_12500 crossref_primary_10_1007_s11042_023_15850_8 crossref_primary_10_1109_TNNLS_2021_3062070 crossref_primary_10_1016_j_future_2021_06_035 crossref_primary_10_2514_1_I011051 crossref_primary_10_1016_j_cmpb_2021_106086 crossref_primary_10_1038_s41597_024_03952_3 crossref_primary_10_1177_14759217211053776 crossref_primary_10_3390_infrastructures7110152 crossref_primary_10_3390_s24175652 crossref_primary_10_1177_03611981211007481 crossref_primary_10_3390_sym14010152 crossref_primary_10_1109_TITS_2022_3204334 crossref_primary_10_3390_buildings12040432 crossref_primary_10_1117_1_JEI_33_6_063027 crossref_primary_10_1111_mice_12954 crossref_primary_10_1111_mice_12710 crossref_primary_10_1016_j_autcon_2022_104139 crossref_primary_10_1038_s41597_024_03263_7 crossref_primary_10_1111_mice_12826 crossref_primary_10_1111_mice_12947 crossref_primary_10_2208_jscejpe_76_2_I_11 crossref_primary_10_3390_s25051449 crossref_primary_10_1109_TITS_2024_3373394 crossref_primary_10_62520_fujece_1421398 crossref_primary_10_1016_j_jreng_2024_04_003 crossref_primary_10_1049_itr2_12369 crossref_primary_10_1007_s10044_024_01314_8 crossref_primary_10_1111_mice_12962 crossref_primary_10_3390_su12030830 crossref_primary_10_1109_JSEN_2022_3181003 crossref_primary_10_3390_s23031657 crossref_primary_10_1007_s41062_023_01250_2 crossref_primary_10_1016_j_dib_2021_107133 crossref_primary_10_1109_ACCESS_2019_2956191 crossref_primary_10_1080_10298436_2023_2180641 crossref_primary_10_1155_2021_3137083 crossref_primary_10_1016_j_tust_2023_105310 crossref_primary_10_3846_jcem_2023_19031 crossref_primary_10_1587_transfun_2022IML0003 crossref_primary_10_3390_s24217076 crossref_primary_10_4236_jtts_2025_151001 crossref_primary_10_1111_mice_12928 crossref_primary_10_1016_j_ijdrr_2024_105091 crossref_primary_10_3390_a16120568 crossref_primary_10_61186_jiaeee_21_3_139 crossref_primary_10_3390_app10010319 crossref_primary_10_1016_j_conbuildmat_2022_129238 crossref_primary_10_1155_2022_3712289 crossref_primary_10_1007_s10921_022_00907_9 crossref_primary_10_3390_buildings14061546 crossref_primary_10_1016_j_autcon_2022_104481 crossref_primary_10_1111_mice_12701 crossref_primary_10_1007_s11760_022_02393_y crossref_primary_10_1080_23789689_2023_2287857 crossref_primary_10_1016_j_aei_2023_102007 crossref_primary_10_3390_math11153277 crossref_primary_10_1111_mice_12815 crossref_primary_10_1080_10298436_2020_1765241 crossref_primary_10_1080_10298436_2023_2219366 crossref_primary_10_3390_s22083044 crossref_primary_10_46632_cset_2_3_5 crossref_primary_10_3390_infrastructures9060090 crossref_primary_10_1061__ASCE_CP_1943_5487_0001013 crossref_primary_10_1111_mice_13200 crossref_primary_10_1155_2024_8846470 crossref_primary_10_1016_j_autcon_2021_103910 crossref_primary_10_1177_1475921720940068 crossref_primary_10_1007_s40996_021_00671_2 crossref_primary_10_1016_j_trgeo_2024_101304 crossref_primary_10_1088_1757_899X_1019_1_012036 crossref_primary_10_3390_app12031374 crossref_primary_10_1155_2023_3555133 crossref_primary_10_1111_mice_12485 crossref_primary_10_3389_fmolb_2023_1147514 crossref_primary_10_1109_TIV_2022_3210299 crossref_primary_10_3390_s20143954 crossref_primary_10_7855_IJHE_2024_26_6_147 crossref_primary_10_3390_su132212682 crossref_primary_10_1109_TITS_2024_3391751 crossref_primary_10_1016_j_autcon_2024_105828 crossref_primary_10_1080_10298436_2022_2057978 crossref_primary_10_1109_JIOT_2020_3024885 crossref_primary_10_48175_IJARSCT_3526 crossref_primary_10_3390_s24134124 crossref_primary_10_1007_s11042_020_10040_2 crossref_primary_10_3390_app10196662 crossref_primary_10_1111_mice_12451 crossref_primary_10_3390_buildings12081081 crossref_primary_10_1155_2022_8392918 crossref_primary_10_35234_fumbd_1003341 crossref_primary_10_1109_ACCESS_2021_3125703 crossref_primary_10_3390_su16219168 crossref_primary_10_1002_2475_8876_12362 crossref_primary_10_1016_j_conbuildmat_2023_132731 crossref_primary_10_1111_mice_12458 crossref_primary_10_1016_j_measurement_2023_113716 crossref_primary_10_1007_s11760_025_03913_2 crossref_primary_10_3390_ijgi12090382 crossref_primary_10_3169_itej_76_78 crossref_primary_10_1016_j_prostr_2023_01_259 crossref_primary_10_3390_s22239366 crossref_primary_10_1016_j_aej_2024_09_097 crossref_primary_10_1016_j_iintel_2022_100004 crossref_primary_10_1109_TMC_2022_3198089 crossref_primary_10_2208_jscejj_24_21019 crossref_primary_10_1088_1742_6596_2115_1_012019 crossref_primary_10_1007_s42107_023_00748_5 crossref_primary_10_1109_TITS_2022_3192916 crossref_primary_10_1007_s13042_020_01078_7 crossref_primary_10_1109_OJITS_2023_3237480 crossref_primary_10_1111_mice_12550 crossref_primary_10_1111_mice_12793 crossref_primary_10_1109_ACCESS_2024_3512783 crossref_primary_10_1016_j_engstruct_2020_111347 crossref_primary_10_3389_fmats_2023_1239263 crossref_primary_10_1016_j_dib_2023_109692 crossref_primary_10_1016_j_asoc_2023_111174 crossref_primary_10_1061_JCCEE5_CPENG_5512 crossref_primary_10_1111_mice_12674 crossref_primary_10_1016_j_autcon_2021_103833 crossref_primary_10_3390_app13095810 crossref_primary_10_1080_10589759_2024_2440816 crossref_primary_10_1016_j_conbuildmat_2023_132684 crossref_primary_10_3390_s24020446 crossref_primary_10_1177_03611981211005450 crossref_primary_10_1080_10298436_2021_1932881 crossref_primary_10_1587_transinf_2019EDP7264 crossref_primary_10_1111_mice_12561 crossref_primary_10_1109_TITS_2022_3221067 crossref_primary_10_1111_mice_12448 crossref_primary_10_1111_mice_12565 crossref_primary_10_1155_2023_9940881 crossref_primary_10_1061_JCCEE5_CPENG_5500 crossref_primary_10_1016_j_heliyon_2024_e24142 crossref_primary_10_3390_s22229019 crossref_primary_10_1016_j_neucom_2025_129661 crossref_primary_10_1155_2021_5395494 crossref_primary_10_1016_j_autcon_2022_104190 crossref_primary_10_1007_s11554_024_01451_7 crossref_primary_10_1016_j_autcon_2024_105405 crossref_primary_10_1111_mice_12536 crossref_primary_10_1016_j_autcon_2024_105643 crossref_primary_10_1111_mice_12532 crossref_primary_10_35414_akufemubid_1328778 crossref_primary_10_1016_j_dsp_2024_104661 crossref_primary_10_3390_s23063268 crossref_primary_10_1111_mice_12530 crossref_primary_10_1016_j_aei_2020_101205 crossref_primary_10_1007_s10462_023_10475_7 crossref_primary_10_1016_j_aei_2024_102378 crossref_primary_10_1061__ASCE_CF_1943_5509_0001606 crossref_primary_10_1016_j_autcon_2021_103935 crossref_primary_10_3390_app142411974 crossref_primary_10_1016_j_jobe_2023_106688 crossref_primary_10_1049_ipr2_12940 crossref_primary_10_1016_j_autcon_2024_105772 crossref_primary_10_1016_j_jag_2023_103335 crossref_primary_10_1177_14759217221084878 crossref_primary_10_3390_app121910089 crossref_primary_10_1109_TITS_2024_3416508 crossref_primary_10_3846_jcem_2024_20401 crossref_primary_10_1111_mice_12546 crossref_primary_10_1111_mice_12667 crossref_primary_10_1111_mice_12541 crossref_primary_10_1016_j_aei_2024_102388 crossref_primary_10_1155_2021_5586615 crossref_primary_10_3390_app14072909 crossref_primary_10_1109_TITS_2024_3474704 crossref_primary_10_1007_s00371_024_03743_2 crossref_primary_10_3390_rs13050943 |
| Cites_doi | 10.1007/s11263-009-0275-4 10.1109/CVPR.2016.90 10.1007/s11263-013-0620-5 10.15446/dyna.v83n195.44919 10.1145/3004725.3004729 10.1109/CVPR.2014.414 10.1111/mice.12042 10.1109/CVPR.2014.81 10.1109/CVPR.2017.351 10.1177/0278364913491297 10.1061/(ASCE)CP.1943-5487.0000736 10.2208/jscejpe.70.I_1 10.1109/TPAMI.2009.167 10.1111/mice.12313 10.1109/BigData.2017.8258427 10.1109/IJCNN.2017.7966101 10.14359/51689560 10.1002/tal.1400 10.1109/CVPR.2009.5206848 10.1109/ICIP.2016.7533052 10.1111/mice.12256 10.1111/mice.12263 10.1016/j.engstruct.2017.10.070 10.1111/mice.12297 10.1109/ICCV.2015.169 10.18100/ijamec.270546 10.1007/s11263-014-0733-5 10.2208/jscejcei.69.I_54 10.1109/CVPR.2016.308 10.1016/j.aej.2017.01.020 10.3390/s151129316 10.1007/978-3-319-10602-1_48 10.1109/CVPR.2017.690 10.1111/0885-9507.00219 10.1109/CVPR.2016.91 10.1007/978-3-319-46448-0_2 10.1111/j.1467-8667.2011.00716.x 10.1111/mice.12098 |
| ContentType | Journal Article |
| Copyright | 2018 2018 Computer‐Aided Civil and Infrastructure Engineering |
| Copyright_xml | – notice: 2018 – notice: 2018 Computer‐Aided Civil and Infrastructure Engineering |
| DBID | AAYXX CITATION 7SC 8FD FR3 JQ2 KR7 L7M L~C L~D |
| DOI | 10.1111/mice.12387 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | CrossRef Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Engineering Computer Science |
| EISSN | 1467-8667 |
| EndPage | 1141 |
| ExternalDocumentID | 10_1111_mice_12387 MICE12387 |
| Genre | article |
| GrantInformation_xml | – fundername: National Institute of Information and Communications Technology (NICT) |
| GroupedDBID | ..I .3N .4S .DC .GA 05W 0R~ 10A 1OB 1OC 29F 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6P2 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AAHQN AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABEML ABFSI ABJNI ACAHQ ACBWZ ACCFJ ACCZN ACGFS ACPOU ACRPL ACSCC ACUHS ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN ADZOD AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFEBI AFFPM AFGKR AFPWT AHBTC AHEFC AI. AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CAG COF CS3 CWDTD D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 E.L EAD EAP EBS EDO EJD EMK EST ESX F00 F01 F04 FEDTE G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IHE IX1 J0M K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MK~ MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 NF~ O66 O9- OIG P2P P2W P2X P4D PALCI Q.N Q11 QB0 R.K RJQFR RX1 SAMSI SUPJJ TN5 TUS UB1 VH1 W8V W99 WBKPD WIH WIK WLBEL WOHZO WQJ WRC WXSBR WYISQ XG1 ZZTAW ~IA ~WT AAMMB AAYXX ADMLS AEFGJ AEYWJ AGHNM AGQPQ AGXDD AGYGG AIDQK AIDYY AIQQE CITATION 7SC 8FD FR3 JQ2 KR7 L7M L~C L~D |
| ID | FETCH-LOGICAL-c3547-770829a3efc82f3e1251139c395e054dfc6b616b82da4a3b44d51893e4a75b343 |
| IEDL.DBID | DR2 |
| ISSN | 1093-9687 |
| IngestDate | Fri Jul 25 02:27:00 EDT 2025 Wed Oct 01 04:15:57 EDT 2025 Thu Apr 24 23:08:09 EDT 2025 Wed Jan 22 16:34:52 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3547-770829a3efc82f3e1251139c395e054dfc6b616b82da4a3b44d51893e4a75b343 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2131892434 |
| PQPubID | 2045171 |
| PageCount | 15 |
| ParticipantIDs | proquest_journals_2131892434 crossref_primary_10_1111_mice_12387 crossref_citationtrail_10_1111_mice_12387 wiley_primary_10_1111_mice_12387_MICE12387 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | December 2018 |
| PublicationDateYYYYMMDD | 2018-12-01 |
| PublicationDate_xml | – month: 12 year: 2018 text: December 2018 |
| PublicationDecade | 2010 |
| PublicationPlace | Hoboken |
| PublicationPlace_xml | – name: Hoboken |
| PublicationTitle | Computer-aided civil and infrastructure engineering |
| PublicationYear | 2018 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | 2010; 32 2015; 15 2013; 69 2017; 26 2015; 70 2013; 104 2009 2008 2014; 29 2017; 114 2016; 4 2010; 88 2013; 32 2015; 111 2017; 32 2018; 156 2018 2017 2016; 83 2016 2001; 16 2015 2014 2012; 27 2013 2018; 32 e_1_2_9_31_1 e_1_2_9_52_1 e_1_2_9_50_1 e_1_2_9_10_1 Dai J. (e_1_2_9_11_1) 2016 e_1_2_9_35_1 e_1_2_9_12_1 e_1_2_9_54_1 AASHTO (e_1_2_9_2_1) 2008 Mertz C. (e_1_2_9_33_1) 2014 Sermanet P. (e_1_2_9_46_1) 2013 e_1_2_9_14_1 e_1_2_9_18_1 Howard A. G. (e_1_2_9_22_1) 2017 Buttlar W. G. (e_1_2_9_5_1) 2014 e_1_2_9_41_1 e_1_2_9_20_1 e_1_2_9_43_1 e_1_2_9_8_1 e_1_2_9_6_1 e_1_2_9_4_1 JRA (e_1_2_9_27_1) 2013 Ioffe S. (e_1_2_9_25_1) 2015 e_1_2_9_26_1 e_1_2_9_49_1 e_1_2_9_28_1 e_1_2_9_30_1 Ren S. (e_1_2_9_45_1) 2015 e_1_2_9_53_1 e_1_2_9_51_1 e_1_2_9_34_1 e_1_2_9_13_1 e_1_2_9_32_1 Radford A. (e_1_2_9_39_1) 2015 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_19_1 e_1_2_9_42_1 e_1_2_9_40_1 e_1_2_9_21_1 e_1_2_9_23_1 e_1_2_9_44_1 e_1_2_9_7_1 Obara M. (e_1_2_9_37_1) 2017 e_1_2_9_3_1 Simonyan K. (e_1_2_9_47_1) 2014 Huval B. (e_1_2_9_24_1) 2015 e_1_2_9_9_1 Fan Z. (e_1_2_9_16_1) 2018 e_1_2_9_48_1 e_1_2_9_29_1 |
| References_xml | – start-page: 448 year: 2015 end-page: 56 – start-page: 21 year: 2016 end-page: 37 – volume: 32 start-page: 361 issue: 5 year: 2017 end-page: 78 article-title: Deep learning‐based crack damage detection using convolutional neural networks publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 69 start-page: I‐54 issue: 2 year: 2013 end-page: I‐62 article-title: An effective surface inspection method of urban roads according to the pavement management situation of local governments publication-title: Journal of Japan Society of Civil Engineers, Ser. F3 (Civil Engineering Informatics) – volume: 15 start-page: 29316 issue: 11 year: 2015 end-page: 31 article-title: Pothole detection system using a black‐box camera publication-title: Sensors – volume: 111 start-page: 98 issue: 1 year: 2015 end-page: 136 article-title: The Pascal visual object classes challenge: a retrospective publication-title: International Journal of Computer Vision – start-page: 2818 year: 2016 end-page: 26 – volume: 32 start-page: 1627 issue: 9 year: 2010 end-page: 45 article-title: Object detection with discriminatively trained part‐based models publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 156 start-page: 598 year: 2018 end-page: 607 article-title: A novel unsupervised deep learning model for global and local health condition assessment of structures publication-title: Engineering Structures – year: 2014 – volume: 32 start-page: 805 issue: 10 year: 2017 end-page: 19 article-title: Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network publication-title: Computer‐Aided Civil and Infrastructure Engineering – start-page: 580 year: 2014 end-page: 87 – start-page: 770 year: 2016 end-page: 78 – volume: 104 start-page: 154 issue: 2 year: 2013 end-page: 71 article-title: Selective search for object recognition publication-title: International Journal of Computer Vision – start-page: 37 year: 2016 end-page: 45 – volume: 26 issue: 18 year: 2017 article-title: A novel machine learning‐based algorithm to detect damage in high‐rise building structures publication-title: The Structural Design of Tall and Special Buildings – volume: 29 start-page: 342 issue: 5 year: 2014 end-page: 58 article-title: Road crack detection using visual features extracted by Gabor filters publication-title: Computer‐Aided Civil and Infrastructure Engineering – year: 2008 – start-page: 4092 year: 2017 end-page: 97 – start-page: 91 year: 2015 end-page: 99 – start-page: 1440 year: 2015 end-page: 48 – volume: 32 start-page: 1025 issue: 12 year: 2017 end-page: 46 article-title: Structural damage detection with automatic feature‐extraction through deep learning publication-title: Computer‐Aided Civil and Infrastructure Engineering – year: 2013 article-title: OverFeat: integrated recognition, localization and detection using convolutional networks publication-title: Computer Vision and Pattern Recognition – volume: 32 start-page: 271 issue: 4 year: 2017 end-page: 87 article-title: A texture‐based video processing methodology using Bayesian data fusion for autonomous crack detection on metallic surfaces publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 32 start-page: 1231 issue: 11 year: 2013 end-page: 37 article-title: Vision meets robotics: the KITTI dataset publication-title: The International Journal of Robotics Research – year: 2015 article-title: Unsupervised representation learning with deep convolutional generative adversarial networks publication-title: Computer Vision and Pattern Recognition – volume: 29 start-page: 644 issue: 9 year: 2014 end-page: 58 article-title: Regionally enhanced multiphase segmentation technique for damaged surfaces publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 88 start-page: 303 issue: 2 year: 2010 end-page: 38 article-title: The Pascal visual object classes (VOC) challenge publication-title: International Journal of Computer Vision – year: 2014 article-title: Very deep convolutional networks for large‐scale image recognition publication-title: Computer Vision and Pattern Recognition – start-page: 3286 year: 2014 end-page: 93 – year: 2017 article-title: Crack detection using image processing: a critical review and analysis publication-title: Alexandria Engineering Journal – year: 2016 – start-page: 379 year: 2016 end-page: 87 – start-page: 2039 year: 2017 end-page: 47 – volume: 83 start-page: 156 issue: 195 year: 2016 end-page: 62 article-title: Detection and localization of potholes in roadways using smartphones publication-title: Dyna – year: 2015 article-title: An empirical evaluation of deep learning on highway driving publication-title: Computer Vision and Pattern Recognition – volume: 114 start-page: 237 issue: 2 year: 2017 end-page: 44 article-title: Supervised deep restricted Boltzmann machine for estimation of concrete publication-title: ACI Materials Journal – start-page: 248 year: 2009 end-page: 55 – volume: 32 start-page: 1 issue: 2 year: 2018 end-page: 12 article-title: Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning publication-title: Journal of Computing in Civil Engineering – volume: 16 start-page: 126 issue: 2 year: 2001 end-page: 42 article-title: Neural networks in civil engineering: 1989–2000 publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 27 start-page: 29 issue: 1 year: 2012 end-page: 47 article-title: Concrete crack detection by multiple sequential image filtering publication-title: Computer‐Aided Civil and Infrastructure Engineering – start-page: 740 year: 2014 end-page: 55 – start-page: 3708 year: 2016 end-page: 12 – year: 2017 – start-page: 1 year: 2014 end-page: 9 – year: 2017 article-title: MobileNets: efficient convolutional neural networks for mobile vision applications publication-title: Computer Vision and Pattern Recognition – start-page: 779 year: 2016 end-page: 88 – volume: 70 start-page: 1 issue: 3 year: 2015 end-page: 8 article-title: Asphalt pavement crack detection using image processing and naïve Bayes based machine learning approach publication-title: Journal of Japan Society of Civil Engineers, Ser. E1 (Pavement Engineering) – volume: 4 start-page: 290 issue: Special Issue‐1 year: 2016 end-page: 95 article-title: A fast and adaptive road defect detection approach using computer vision with real time implementation publication-title: International Journal of Applied Mathematics, Electronics and Computers – year: 2018 article-title: Automatic pavement crack detection based on structured prediction with the convolutional neural network publication-title: Computer Vision and Pattern Recognition – year: 2013 – ident: e_1_2_9_15_1 doi: 10.1007/s11263-009-0275-4 – ident: e_1_2_9_21_1 doi: 10.1109/CVPR.2016.90 – ident: e_1_2_9_50_1 doi: 10.1007/s11263-013-0620-5 – ident: e_1_2_9_6_1 doi: 10.15446/dyna.v83n195.44919 – ident: e_1_2_9_32_1 doi: 10.1145/3004725.3004729 – ident: e_1_2_9_9_1 doi: 10.1109/CVPR.2014.414 – ident: e_1_2_9_51_1 doi: 10.1111/mice.12042 – year: 2015 ident: e_1_2_9_39_1 article-title: Unsupervised representation learning with deep convolutional generative adversarial networks publication-title: Computer Vision and Pattern Recognition – ident: e_1_2_9_20_1 doi: 10.1109/CVPR.2014.81 – ident: e_1_2_9_23_1 doi: 10.1109/CVPR.2017.351 – ident: e_1_2_9_18_1 doi: 10.1177/0278364913491297 – year: 2014 ident: e_1_2_9_47_1 article-title: Very deep convolutional networks for large‐scale image recognition publication-title: Computer Vision and Pattern Recognition – ident: e_1_2_9_53_1 doi: 10.1061/(ASCE)CP.1943-5487.0000736 – ident: e_1_2_9_10_1 doi: 10.2208/jscejpe.70.I_1 – start-page: 448 volume-title: Proceedings of the International Conference on Machine Learning year: 2015 ident: e_1_2_9_25_1 – ident: e_1_2_9_17_1 doi: 10.1109/TPAMI.2009.167 – ident: e_1_2_9_30_1 doi: 10.1111/mice.12313 – ident: e_1_2_9_28_1 doi: 10.1109/BigData.2017.8258427 – ident: e_1_2_9_13_1 doi: 10.1109/IJCNN.2017.7966101 – volume-title: Maintenance and Repair Guide Book of the Pavement 2013 year: 2013 ident: e_1_2_9_27_1 – start-page: 1 volume-title: Proceedings of ITS World Congress year: 2014 ident: e_1_2_9_33_1 – year: 2017 ident: e_1_2_9_22_1 article-title: MobileNets: efficient convolutional neural networks for mobile vision applications publication-title: Computer Vision and Pattern Recognition – ident: e_1_2_9_42_1 doi: 10.14359/51689560 – ident: e_1_2_9_40_1 doi: 10.1002/tal.1400 – ident: e_1_2_9_12_1 doi: 10.1109/CVPR.2009.5206848 – volume-title: Proceedings of The Third International Conference on Smart Portable, Wearable, Implantable and Disability‐Oriented Devices and Systems (SPWID 2017) year: 2017 ident: e_1_2_9_37_1 – ident: e_1_2_9_54_1 doi: 10.1109/ICIP.2016.7533052 – ident: e_1_2_9_8_1 doi: 10.1111/mice.12256 – ident: e_1_2_9_7_1 doi: 10.1111/mice.12263 – ident: e_1_2_9_41_1 doi: 10.1016/j.engstruct.2017.10.070 – start-page: 379 volume-title: Proceedings of the Neural Information Processing Systems Conference year: 2016 ident: e_1_2_9_11_1 – ident: e_1_2_9_52_1 doi: 10.1111/mice.12297 – ident: e_1_2_9_19_1 doi: 10.1109/ICCV.2015.169 – volume-title: Bridging the Gap–Restoring and Rebuilding the Nation's Bridges year: 2008 ident: e_1_2_9_2_1 – ident: e_1_2_9_4_1 doi: 10.18100/ijamec.270546 – ident: e_1_2_9_14_1 doi: 10.1007/s11263-014-0733-5 – ident: e_1_2_9_49_1 doi: 10.2208/jscejcei.69.I_54 – ident: e_1_2_9_48_1 doi: 10.1109/CVPR.2016.308 – ident: e_1_2_9_35_1 doi: 10.1016/j.aej.2017.01.020 – ident: e_1_2_9_26_1 doi: 10.3390/s151129316 – ident: e_1_2_9_29_1 doi: 10.1007/978-3-319-10602-1_48 – start-page: 91 volume-title: Proceedings of the Advances in Neural Information Processing Systems year: 2015 ident: e_1_2_9_45_1 – year: 2018 ident: e_1_2_9_16_1 article-title: Automatic pavement crack detection based on structured prediction with the convolutional neural network publication-title: Computer Vision and Pattern Recognition – volume-title: Integration of Smart‐Phone‐Based Pavement Roughness Data Collection Tool with Asset Management System year: 2014 ident: e_1_2_9_5_1 – year: 2015 ident: e_1_2_9_24_1 article-title: An empirical evaluation of deep learning on highway driving publication-title: Computer Vision and Pattern Recognition – ident: e_1_2_9_44_1 doi: 10.1109/CVPR.2017.690 – year: 2013 ident: e_1_2_9_46_1 article-title: OverFeat: integrated recognition, localization and detection using convolutional networks publication-title: Computer Vision and Pattern Recognition – ident: e_1_2_9_3_1 doi: 10.1111/0885-9507.00219 – ident: e_1_2_9_43_1 doi: 10.1109/CVPR.2016.91 – ident: e_1_2_9_31_1 doi: 10.1007/978-3-319-46448-0_2 – ident: e_1_2_9_34_1 – ident: e_1_2_9_36_1 doi: 10.1111/j.1467-8667.2011.00716.x – ident: e_1_2_9_38_1 doi: 10.1111/mice.12098 |
| SSID | ssj0000443 |
| Score | 2.6572983 |
| Snippet | Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1127 |
| SubjectTerms | Artificial neural networks Damage detection Datasets Image classification Image detection Image processing Neural networks Object recognition Smartphones Traffic accidents & safety |
| Title | Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fmice.12387 https://www.proquest.com/docview/2131892434 |
| Volume | 33 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: EBSCOhost Academic Search Ultimate customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1467-8667 dateEnd: 20241105 omitProxy: true ssIdentifier: ssj0000443 issn: 1093-9687 databaseCode: ABDBF dateStart: 19980101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1467-8667 dateEnd: 20241105 omitProxy: false ssIdentifier: ssj0000443 issn: 1093-9687 databaseCode: ADMLS dateStart: 19980101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVWIB databaseName: Wiley Online Library - Core collection (SURFmarket) issn: 1093-9687 databaseCode: DR2 dateStart: 19970101 customDbUrl: isFulltext: true eissn: 1467-8667 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000443 providerName: Wiley-Blackwell |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fS8MwED6GT_rgdCpOpwT0RaGDNmnWgi_iNqbgHqaDIUjJr77o5rDdi3-9l7TdpoigbyVc0zSXy30J390BnCtp0O-Z2ItSwz0mUu5FFO1RqA4P0b9Ro2008v2QD8bsbhJOanBVxcIU-SGWF27WMtx-bQ1cyGzNyG219jbuu5ENJfcpd-ep0VruKFay62PqxTzqlLlJLY1n9epXb7SCmOtA1Xmafh2eqzEWBJOX9iKXbfXxLX3jf39iB7ZLCEquizWzCzUza0C9hKOkNPYMm6qKD1VbA7bW0hfuwdPoTWjSFVPck0jX5I7VNSNipomrtWlZSE7xxBETUMTMic0Ggl8fFvTzjNiLYPIwxRVsWfKG3Nresn0Y93uPNwOvrNXgKRqyDoJ0G6QrqElVFKTUWNyE4FLRODSICnWquOQ-l1GgBRNUMqZDH7GSYaITSsroAWzM8CuHQGTg01SEeG7XhmnpS2ZRjdEI3OI0VUETLiqdJapMZG7rabwm1YHGzmriZrUJZ0vZeZG-40epVqX6pDThLMFR4AADRlkTLp0Of-khQZvpuaejvwgfwyYCsKigx7RgI39fmBMEObk8dYv5E4gs93M |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1JS8QwFH64HNSDuziuAb0odGCaNG2P4sK4zcEFxEvJ1ovOKHa8-Ot9L011FBH0VkKatHl5eV_Cl-8B7BrtMO65PMpKJyOhShllHP1RmVQmGN-4s3Qb-bInu7fi7C65C9wcugtT60N8HLiRZ_j1mhycDqRHvJzStbdx4c3ScZgUEjcqhImuRtSjRODX5zzKZZYGdVIi8ny--zUefYLMUajqY83JXJ1QtfIShUQxeWi_DnXbvH0TcPz3b8zDbECh7KCeNgsw5gaLMBcQKQv-XmFRk_ShKVuEmREFwyW4v3pSlh2pPi5L7MgNPbFrwNTAMp9uk4hI3vbMcxOwintmJAiCvfdqBnrF6CyYXfdxEhNR3rFTaq1ahtuT45vDbhTSNUSGJyJFnE73dBV3pcnikjuCTogvDc8Th8DQlkZq2ZE6i60SimshbNJBuOSEShPNBV-BiQH2sgpMxx1eqgS37tYJqztaELBxFrFbXpYmbsFeY7TCBC1zSqnxWDR7GhrVwo9qC3Y-6j7XCh4_1tpobF8EL64K_Ar8wFhw0YJ9b8RfWijQbY7909pfKm_DVPfm8qK4OO2dr8M04rGsZstswMTw5dVtIuYZ6i0_s98BDbf7lA |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LaxsxEB5SB0J7yMNNqVM3ESSXFNbglfZ1DHVMnIcJTgKhl0XPS2vXdO1Lf31mtFrHKaWQ3hahlXY1Gs0n8ekbgBOtLMY9W0S5s2kkpEujnKM_Sp2lCcY3bg3dRr4ZpxcP4vIxeQzcHLoLU-tDrA7cyDP8ek0ObufGrXk5pWvv4cKbZ29gUyRFToy-wWRNPUoEfn3BoyLNs6BOSkSe53dfxqNnkLkOVX2sGe7UCVUrL1FIFJPvveVC9fTvPwQc__s3dmE7oFB2Vk-bPdiwszbsBETKgr9XWNQkfWjK2vBuTcHwPXyb_JSGDeQUlyU2sAtP7JoxOTPMp9skIpK3PfPcBKxi54wEQbD3cc1ArxidBbO7KU5iIspbNqLWqn14GJ7ff72IQrqGSPNEZIjT6Z6u5NbpPHbcEnRCfKl5kVgEhsbpVKX9VOWxkUJyJYRJ-giXrJBZorjgH6A1w14-AlNxnzuZ4NbdWGFUXwkCNtYgdiuc03EHThujlTpomVNKjR9ls6ehUS39qHbgeFV3Xit4_LVWt7F9Gby4KvEr8ANjwUUHvngj_qOFEt3m3D8dvKbyEWzdDobl9Wh89QneIhzLa7JMF1qLX0v7GSHPQh36if0Eu177GA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Road+Damage+Detection+and+Classification+Using+Deep+Neural+Networks+with+Smartphone+Images&rft.jtitle=Computer-aided+civil+and+infrastructure+engineering&rft.au=Maeda%2C+Hiroya&rft.au=Sekimoto%2C+Yoshihide&rft.au=Seto%2C+Toshikazu&rft.au=Kashiyama%2C+Takehiro&rft.date=2018-12-01&rft.issn=1093-9687&rft.eissn=1467-8667&rft.volume=33&rft.issue=12&rft.spage=1127&rft.epage=1141&rft_id=info:doi/10.1111%2Fmice.12387&rft.externalDBID=n%2Fa&rft.externalDocID=10_1111_mice_12387 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1093-9687&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1093-9687&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1093-9687&client=summon |