Multi‐Fatigue Feature Selection and Fuzzy Logic‐Based Intelligent Driver Drowsiness Detection
Driver drowsiness poses a critical threat, frequently resulting in highly perilous traffic accidents. The drowsiness detection is complicated by various challenges such as lighting conditions, occluded facial features, eyeglasses, and false alarms, making the accuracy, robustness across environments...
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| Published in | IET image processing Vol. 19; no. 1 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
01.01.2025
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| Online Access | Get full text |
| ISSN | 1751-9659 1751-9667 1751-9667 |
| DOI | 10.1049/ipr2.70052 |
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| Abstract | Driver drowsiness poses a critical threat, frequently resulting in highly perilous traffic accidents. The drowsiness detection is complicated by various challenges such as lighting conditions, occluded facial features, eyeglasses, and false alarms, making the accuracy, robustness across environments, and computational efficiency a major challenge. This study proposes a non‐intrusive driver drowsiness detection system, leveraging image processing techniques and advanced fuzzy logic methods. It also introduces improvements to the Viola‐Jones algorithm for swift and precise driver face, eye, and mouth identification. Extensive experiments involving diverse individuals and scenarios were conducted to assess the system's performance in detecting eye and mouth states. The results are highly promising, with eye detection accuracy at 91.8% and mouth detection achieving a remarkable 94.6%, surpassing existing methods. Real‐time testing in varied conditions, including day and night scenarios and subjects with and without glasses, demonstrated the system's robustness, yielding a 97.5% test accuracy in driver drowsiness detection. |
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| AbstractList | Driver drowsiness poses a critical threat, frequently resulting in highly perilous traffic accidents. The drowsiness detection is complicated by various challenges such as lighting conditions, occluded facial features, eyeglasses, and false alarms, making the accuracy, robustness across environments, and computational efficiency a major challenge. This study proposes a non‐intrusive driver drowsiness detection system, leveraging image processing techniques and advanced fuzzy logic methods. It also introduces improvements to the Viola‐Jones algorithm for swift and precise driver face, eye, and mouth identification. Extensive experiments involving diverse individuals and scenarios were conducted to assess the system's performance in detecting eye and mouth states. The results are highly promising, with eye detection accuracy at 91.8% and mouth detection achieving a remarkable 94.6%, surpassing existing methods. Real‐time testing in varied conditions, including day and night scenarios and subjects with and without glasses, demonstrated the system's robustness, yielding a 97.5% test accuracy in driver drowsiness detection. |
| Author | Arava, Mohan Sundaram, Divya Meena |
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| Cites_doi | 10.1109/TIV.2023.3321891 10.1016/j.ijnurstu.2020.103600 10.1016/j.asoc.2014.01.020 10.1016/j.mlwa.2023.100510 10.1109/ICSMC.2010.5641788 10.1016/j.inffus.2017.11.005 10.3390/safety6040055 10.1109/TMM.2020.2985536 10.1177/1748006X221118448 10.1109/TMM.2021.3128738 10.1109/TITS.2018.2868499 10.1109/ICUFN49451.2021.9528706 10.3390/brainsci11020240 10.1109/ACCESS.2023.3287491 10.1016/j.comnet.2018.06.007 10.1016/B978-0-12-815739-8.00010-9 10.1109/TITS.2023.3304128 10.1016/j.neucom.2023.126709 10.1101/2023.12.21.23300419 10.1007/978-3-030-03801-4_38 10.3390/wevj15030099 10.1080/15389588.2019.1706088 10.1049/cvi2.12252 10.1016/j.jshs.2023.09.010 10.1016/j.aap.2020.105955 10.3390/s16111805 10.1117/1.JRS.14.026521 10.3390/s21062026 10.1109/CVPRW.2019.00027 10.1109/TITS.2023.3346054 10.1016/j.aej.2023.01.017 10.1109/TNSRE.2021.3051958 10.3390/s20154093 10.36227/techrxiv.24428014.v1 10.1007/s11571-022-09898-9 10.1007/s12652-021-03311-9 10.3390/s23041874 10.1007/s12193-023-00408-7 10.7717/peerj.15744 10.1109/TIV.2024.3405990 10.1063/5.0212719 10.1109/iFUZZY63051.2024.10661369 10.3390/s21165558 10.7717/peerj‐cs.2447 10.1007/s00521-023-09224-2 10.1109/ACCESS.2024.3424654 |
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| References | e_1_2_9_31_1 e_1_2_9_50_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_12_1 e_1_2_9_33_1 Bakishev K. A. (e_1_2_9_3_1) 2017; 8 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_18_1 e_1_2_9_41_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_45_1 e_1_2_9_24_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 Arava M. (e_1_2_9_25_1) 2024; 12 e_1_2_9_26_1 e_1_2_9_49_1 e_1_2_9_28_1 e_1_2_9_47_1 e_1_2_9_30_1 e_1_2_9_51_1 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_13_1 e_1_2_9_32_1 Abtahi S. (e_1_2_9_52_1) 2020 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_46_1 e_1_2_9_23_1 e_1_2_9_44_1 e_1_2_9_7_1 e_1_2_9_5_1 Min J. (e_1_2_9_21_1) 2023; 35 AlKishri W. (e_1_2_9_2_1) 2022 e_1_2_9_9_1 e_1_2_9_27_1 e_1_2_9_48_1 e_1_2_9_29_1 |
| References_xml | – ident: e_1_2_9_36_1 doi: 10.1109/TIV.2023.3321891 – ident: e_1_2_9_8_1 doi: 10.1016/j.ijnurstu.2020.103600 – ident: e_1_2_9_44_1 doi: 10.1016/j.asoc.2014.01.020 – ident: e_1_2_9_18_1 doi: 10.1016/j.mlwa.2023.100510 – ident: e_1_2_9_45_1 doi: 10.1109/ICSMC.2010.5641788 – ident: e_1_2_9_6_1 doi: 10.1016/j.inffus.2017.11.005 – ident: e_1_2_9_9_1 doi: 10.3390/safety6040055 – ident: e_1_2_9_12_1 doi: 10.1109/TMM.2020.2985536 – ident: e_1_2_9_13_1 doi: 10.1177/1748006X221118448 – ident: e_1_2_9_23_1 doi: 10.1109/TMM.2021.3128738 – ident: e_1_2_9_14_1 doi: 10.1109/TITS.2018.2868499 – ident: e_1_2_9_42_1 doi: 10.1109/ICUFN49451.2021.9528706 – ident: e_1_2_9_47_1 doi: 10.3390/brainsci11020240 – ident: e_1_2_9_37_1 doi: 10.1109/ACCESS.2023.3287491 – ident: e_1_2_9_43_1 doi: 10.1016/j.comnet.2018.06.007 – ident: e_1_2_9_17_1 doi: 10.1016/B978-0-12-815739-8.00010-9 – ident: e_1_2_9_32_1 doi: 10.1109/TITS.2023.3304128 – ident: e_1_2_9_29_1 doi: 10.1016/j.neucom.2023.126709 – ident: e_1_2_9_7_1 doi: 10.1101/2023.12.21.23300419 – year: 2022 ident: e_1_2_9_2_1 article-title: Enhanced Image Processing and Fuzzy Logic Approach for Optimizing Driver Drowsiness Detection publication-title: Applied Computational Intelligence and Soft Computing – ident: e_1_2_9_50_1 doi: 10.1007/978-3-030-03801-4_38 – ident: e_1_2_9_38_1 doi: 10.3390/wevj15030099 – ident: e_1_2_9_10_1 doi: 10.1080/15389588.2019.1706088 – ident: e_1_2_9_40_1 doi: 10.1049/cvi2.12252 – volume: 35 start-page: 8859 issue: 12 year: 2023 ident: e_1_2_9_21_1 article-title: Fusion of Forehead EEG with Machine Vision for Real‐Time Fatigue Detection in an Automatic Processing Pipeline publication-title: Neural Computing and Applications – ident: e_1_2_9_19_1 doi: 10.1016/j.jshs.2023.09.010 – ident: e_1_2_9_4_1 doi: 10.1016/j.aap.2020.105955 – ident: e_1_2_9_30_1 doi: 10.3390/s16111805 – volume-title: YawDD: Yawning Detection Dataset year: 2020 ident: e_1_2_9_52_1 – ident: e_1_2_9_26_1 doi: 10.1117/1.JRS.14.026521 – ident: e_1_2_9_41_1 doi: 10.3390/s21062026 – ident: e_1_2_9_51_1 doi: 10.1109/CVPRW.2019.00027 – ident: e_1_2_9_28_1 doi: 10.1109/TITS.2023.3346054 – ident: e_1_2_9_16_1 doi: 10.1016/j.aej.2023.01.017 – ident: e_1_2_9_33_1 doi: 10.1109/TNSRE.2021.3051958 – ident: e_1_2_9_46_1 doi: 10.3390/s20154093 – ident: e_1_2_9_20_1 doi: 10.36227/techrxiv.24428014.v1 – ident: e_1_2_9_5_1 doi: 10.1007/s11571-022-09898-9 – ident: e_1_2_9_22_1 doi: 10.1007/s12652-021-03311-9 – ident: e_1_2_9_35_1 doi: 10.3390/s23041874 – ident: e_1_2_9_15_1 doi: 10.1007/s12193-023-00408-7 – ident: e_1_2_9_24_1 doi: 10.7717/peerj.15744 – volume: 12 start-page: 437 issue: 11 year: 2024 ident: e_1_2_9_25_1 article-title: Enhancing Driver Drowsiness Detection: A Fusion of Facial Landmarks and Modified YOLOv5 Architecture publication-title: International Journal of Intelligent Systems and Applications in Engineering – ident: e_1_2_9_34_1 doi: 10.1109/TIV.2024.3405990 – ident: e_1_2_9_39_1 doi: 10.1063/5.0212719 – ident: e_1_2_9_48_1 doi: 10.1109/iFUZZY63051.2024.10661369 – ident: e_1_2_9_11_1 doi: 10.3390/s21165558 – ident: e_1_2_9_49_1 doi: 10.7717/peerj‐cs.2447 – volume: 8 start-page: 1456 issue: 5 year: 2017 ident: e_1_2_9_3_1 article-title: Analysis and Prediction of the state of Road Accidents and Traffic Crimes in the Republic of Kazakhstan publication-title: Journal of Advanced Research in Law and Economics – ident: e_1_2_9_27_1 doi: 10.1007/s00521-023-09224-2 – ident: e_1_2_9_31_1 doi: 10.1109/ACCESS.2024.3424654 |
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| Title | Multi‐Fatigue Feature Selection and Fuzzy Logic‐Based Intelligent Driver Drowsiness Detection |
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