Research on high-precision detection technology of box trucks based on improved YOLOv5

Weigao Qiao; Shuangwen Liu; Jianzhong Zhang; Fei Yuan

doi:10.1117/12.3037810

12 September 2024 Research on high-precision detection technology of box trucks based on improved YOLOv5

Weigao Qiao, Shuangwen Liu, Jianzhong Zhang, Fei Yuan

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 132561N (2024) https://doi.org/10.1117/12.3037810
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

This study focuses on enhancing the YOLOv5 algorithm for real-time vehicle detection, critical for autonomous driving and surveillance. We improved its performance for box truck detection through advanced attention mechanisms, multi-scale feature fusion, and lightweight design. These enhancements led to notable increases in accuracy metrics (mAP@0.5 from 0.731 to 0.771 and mAP@0.5:0.95 from 0.537 to 0.56) and improved both precision and recall. This demonstrates the optimization's theoretical and practical effectiveness, contributing significantly to autonomous driving and intelligent transportation safety and reliability. The study offers valuable technical insights for deep learning and computer vision, guiding future advancements in vehicle detection and visual recognition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Weigao Qiao, Shuangwen Liu, Jianzhong Zhang, and Fei Yuan "Research on high-precision detection technology of box trucks based on improved YOLOv5", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 132561N (12 September 2024); https://doi.org/10.1117/12.3037810

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