基于深度学习轻量化的改进SSD煤矸快速分选模型
TD94; 针对SSD目标检测模型参数量大、运行速率低的问题,在SSD模型的基础上提出一种新的煤矸快速识别模型DSR-SSD.应用深度可分离卷积代替主干特征提取网络中的普通卷积,减少了模型的计算量;将RFB模块融入到SSD模型中,提高了模型的特征提取能力.经验证,DSR-SSD模型的识别速率为113.99帧/s、精确率为95.17%.将DSR-SSD与SSD,Faster-RCNN,YOLOv3 三种模型对比,发现DSR-SSD模型与SSD模型相比,精确率提高了2.29%,识别速率提高了60.89%;同时,DSR-SSD模型的精确率比Faster-RCNN模型高 2.86%,比YOLOv3 模...
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| Published in | 东北大学学报（自然科学版） Vol. 44; no. 10; pp. 1474 - 1480 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
太原理工大学 煤矿综采装备山西省重点实验室,山西 太原 030024
01.10.2023
太原理工大学 机械与运载工程学院,山西 太原 030024 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1005-3026 |
| DOI | 10.12068/j.issn.1005-3026.2023.10.014 |
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| Abstract | TD94; 针对SSD目标检测模型参数量大、运行速率低的问题,在SSD模型的基础上提出一种新的煤矸快速识别模型DSR-SSD.应用深度可分离卷积代替主干特征提取网络中的普通卷积,减少了模型的计算量;将RFB模块融入到SSD模型中,提高了模型的特征提取能力.经验证,DSR-SSD模型的识别速率为113.99帧/s、精确率为95.17%.将DSR-SSD与SSD,Faster-RCNN,YOLOv3 三种模型对比,发现DSR-SSD模型与SSD模型相比,精确率提高了2.29%,识别速率提高了60.89%;同时,DSR-SSD模型的精确率比Faster-RCNN模型高 2.86%,比YOLOv3 模型高 2.71%,识别速率分别是Faster-RCNN模型和YOLOv3 模型的14.90 倍和3.65 倍,证明了DSR-SSD模型性能优越. |
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| AbstractList | TD94; 针对SSD目标检测模型参数量大、运行速率低的问题,在SSD模型的基础上提出一种新的煤矸快速识别模型DSR-SSD.应用深度可分离卷积代替主干特征提取网络中的普通卷积,减少了模型的计算量;将RFB模块融入到SSD模型中,提高了模型的特征提取能力.经验证,DSR-SSD模型的识别速率为113.99帧/s、精确率为95.17%.将DSR-SSD与SSD,Faster-RCNN,YOLOv3 三种模型对比,发现DSR-SSD模型与SSD模型相比,精确率提高了2.29%,识别速率提高了60.89%;同时,DSR-SSD模型的精确率比Faster-RCNN模型高 2.86%,比YOLOv3 模型高 2.71%,识别速率分别是Faster-RCNN模型和YOLOv3 模型的14.90 倍和3.65 倍,证明了DSR-SSD模型性能优越. |
| Author | 李博 魏代良 文小 李娟莉 |
| AuthorAffiliation | 太原理工大学 机械与运载工程学院,山西 太原 030024;太原理工大学 煤矿综采装备山西省重点实验室,山西 太原 030024 |
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| Author_FL | LI Bo LI Juan-li WEN Xiao WEI Dai-liang |
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| DocumentTitle_FL | Improved SSD Rapid Separation Model of Coal Gangue Based on Deep Learning and Light-Weighting |
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| Keywords | deep learning target detection SSD model coal gangue separation 深度学习 目标检测 SSD模型 轻量化 煤矸分选 light-weighting |
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| Publisher | 太原理工大学 煤矿综采装备山西省重点实验室,山西 太原 030024 太原理工大学 机械与运载工程学院,山西 太原 030024 |
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| Title | 基于深度学习轻量化的改进SSD煤矸快速分选模型 |
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