A lightweight vehicles detection network model based on YOLOv5

Jiale Xu; Songyan Liu; Yao Liu; Fangpeng Lu

doi:10.1117/12.2685074

19 October 2023 A lightweight vehicles detection network model based on YOLOv5

Jiale Xu, Songyan Liu, Yao Liu, Fangpeng Lu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 1270939 (2023) https://doi.org/10.1117/12.2685074
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

For the existing target detection algorithm with large number of parameters and computation, which cannot be broadly applicable in the vehicle detection field, a lightweight vehicle detection network model is designed in this paper. The vehicle detection model mainly uses RFB-PANet to replace the PANet feature fusion network of YOLOv5s to obtain rich feature information to make the model more accurate, and uses GhostConv structure to optimize the traditional convolutional layer of YOLOv5s to have less parameters and computation of the model, so as to achieve the purpose of lightweights vehicle detection model. The experimental results show that proposed vehicle detection network model reduces the number of parameters by 25.0% and the computational effort by 18.75% compared with the original YOLOV5 network model. The mAp is increased by 2.1% and 1.2% on Pascal VOC dataset and vehicle dataset, respectively, thus achieving a good balance of detection accuracy and light weight

Citation Download Citation

Jiale Xu, Songyan Liu, Yao Liu, and Fangpeng Lu "A lightweight vehicles detection network model based on YOLOv5", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 1270939 (19 October 2023); https://doi.org/10.1117/12.2685074

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