Research on vehicle target detection in blizzard scene based on improved YOLOv5s

Xinying Dong; Piao Cheng; Shuke An; Rongsheng Fan; Lianghui Qian

doi:10.1117/12.3038459

23 August 2024 Research on vehicle target detection in blizzard scene based on improved YOLOv5s

Xinying Dong, Piao Cheng, Shuke An, Rongsheng Fan, Lianghui Qian

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132501E (2024) https://doi.org/10.1117/12.3038459
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

In the field of autonomous driving, object detection is the core foundation of autonomous driving environment perception, and it is also the premise for the safe operation of the whole system, and the accuracy of object detection in bad weather is also a hot research content in the entire industry. To this end, this paper proposes an improved algorithm for YOLOv5s in Blizzard scenarios. (1) The C3 module in the YOLOv5s backbone network is replaced with a C2f module with a richer gradient flow to improve the detection accuracy of small targets. (2)The attention mechanism SE was introduced to adjust the channel weight, which strengthened the extraction of important features of the vehicle. (3) The loss function C_IOU is replaced with E_IOU, which accelerates the convergence of the prediction frame and improves the regression accuracy. And the data set constructed in this paper is verified, and the results show that: Compared with YOLOv5s, the accuracy of the algorithm proposed in this paper is increased by 6.7% to 91.3%. The recall rate increased by 2% to 72.2%; The mAP@.5 value increased by 3.5% to 81.9%,The detection performance of the model in Blizzard scenarios is effectively improved.

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

Citation Download Citation

Xinying Dong, Piao Cheng, Shuke An, Rongsheng Fan, and Lianghui Qian "Research on vehicle target detection in blizzard scene based on improved YOLOv5s", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132501E (23 August 2024); https://doi.org/10.1117/12.3038459

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KEYWORDS

Target detection

Detection and tracking algorithms

Object detection

Feature extraction

Machine learning

Performance modeling

Statistical modeling

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