AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm
Aiming at the high missed detection rate of small target vehicles and the heterogeneous redundant frames in video vehicle detection，a real-time vehicle detection algorithm based on improved YOLOv5s was proposed. To improve the detection rate of small target vehicles，an optimization of the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2024-02-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2300 |
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| Summary: | Aiming at the high missed detection rate of small target vehicles and the heterogeneous redundant frames in video vehicle detection，a real-time vehicle detection algorithm based on improved YOLOv5s was proposed. To improve the detection rate of small target vehicles，an optimization of the YOLOv5s algorithm network structure was established，which added a small target detection layer and spliced the shallow feature map with the deep feature map in the detection. For the problem of heterogeneous redundant frames，weighted non-maximum value suppression is used to fuse the information of both frames to improve the detection accuracy. The experimental results show that the average detection accuracy ( mAP @ 0. 5 ∶ 0. 95 ) of the improved YOLOv5s algorithm reaches 64. 17% . Compared with the YOLOv5s algorithm，the precision and recall rate are improved by 1. 72% and 0. 72% respectively. In the small target vehicle detection，the positive detection rate is increased by 5. 95% and the missed detection rate is reduced by 4. 63% . The improved YOLOv5s algorithm can effectively improve the detection precision and accuracy of small target vehicles. |
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| ISSN: | 1007-2683 |