YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection
In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/15/5903 |
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| author | Haohui Lv Hanbing Yan Keyang Liu Zhenwu Zhou Junjie Jing |
| author_facet | Haohui Lv Hanbing Yan Keyang Liu Zhenwu Zhou Junjie Jing |
| author_sort | Haohui Lv |
| collection | DOAJ |
| description | In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate of target pedestrians, and the poor retention of non-redundant boxes, YOLOv5 is adopted as the baseline to improve the effectiveness of pedestrian detection. First of all, L1 regularization is deployed before the BN layer, and the layers with smaller influence factors are removed through sparse training to achieve the effect of model pruning. In the next moment, the context extraction module is applied to the feature extraction network, and the input features are fully extracted using receptive fields of different sizes. In addition, both the context attention module CxAM and the content attention module CnAM are added to the FPN part to correct the target position deviation in the process of feature extraction so that the accuracy of detection can be improved. What is more, DIoU_NMS is employed to replace NMS as the prediction frame screening algorithm to improve the problem of detection target loss in the case of high target coincidence. Experimental results show that compared with YOLOv5, the AP of our YOLOv5-AC model for pedestrians is 95.14%, the recall is 94.22%, and the counting frame rate is 63.1 FPS. Among them, AP and recall increased by 3.78% and 3.92%, respectively, while the detection speed increased by 57.8%. The experimental results verify that our YOLOv5-AC is an effective and accurate method for pedestrian detection in railways. |
| first_indexed | 2024-03-09T12:10:15Z |
| format | Article |
| id | doaj.art-080588d4c541414bb72b85a45d85ed3b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:10:15Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-080588d4c541414bb72b85a45d85ed3b2023-11-30T22:52:36ZengMDPI AGSensors1424-82202022-08-012215590310.3390/s22155903YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian DetectionHaohui Lv0Hanbing Yan1Keyang Liu2Zhenwu Zhou3Junjie Jing4School of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu 610225, ChinaIn response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate of target pedestrians, and the poor retention of non-redundant boxes, YOLOv5 is adopted as the baseline to improve the effectiveness of pedestrian detection. First of all, L1 regularization is deployed before the BN layer, and the layers with smaller influence factors are removed through sparse training to achieve the effect of model pruning. In the next moment, the context extraction module is applied to the feature extraction network, and the input features are fully extracted using receptive fields of different sizes. In addition, both the context attention module CxAM and the content attention module CnAM are added to the FPN part to correct the target position deviation in the process of feature extraction so that the accuracy of detection can be improved. What is more, DIoU_NMS is employed to replace NMS as the prediction frame screening algorithm to improve the problem of detection target loss in the case of high target coincidence. Experimental results show that compared with YOLOv5, the AP of our YOLOv5-AC model for pedestrians is 95.14%, the recall is 94.22%, and the counting frame rate is 63.1 FPS. Among them, AP and recall increased by 3.78% and 3.92%, respectively, while the detection speed increased by 57.8%. The experimental results verify that our YOLOv5-AC is an effective and accurate method for pedestrian detection in railways.https://www.mdpi.com/1424-8220/22/15/5903pedestrian detectiondeep learningmodel pruningcontext extraction moduleattention moduleDIoU_NMS |
| spellingShingle | Haohui Lv Hanbing Yan Keyang Liu Zhenwu Zhou Junjie Jing YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection Sensors pedestrian detection deep learning model pruning context extraction module attention module DIoU_NMS |
| title | YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection |
| title_full | YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection |
| title_fullStr | YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection |
| title_full_unstemmed | YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection |
| title_short | YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection |
| title_sort | yolov5 ac attention mechanism based lightweight yolov5 for track pedestrian detection |
| topic | pedestrian detection deep learning model pruning context extraction module attention module DIoU_NMS |
| url | https://www.mdpi.com/1424-8220/22/15/5903 |
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