Improved vehicle detection based on YOLOv5s

Cuihua Tian; Yawei Chen; Zhihui Li; Minghui Fan

doi:10.1117/12.2674439

28 April 2023 Improved vehicle detection based on YOLOv5s

Cuihua Tian, Yawei Chen, Zhihui Li, Minghui Fan

Proceedings Volume 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022); 126261C (2023) https://doi.org/10.1117/12.2674439
Event: International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 2022, Zhuhai, China

Abstract

Aiming at the problems of low detection accuracy, poor real-time and robustness in vehicle target detection in the field of autonomous driving and other fields of existing target detection algorithms, this paper proposes a model based on YOLOv5s and introduces an attention mechanism to fuse multi-scale features. The detection algorithm of YOLOv5s model adds a layer of CBAM(Convolutional Block Attention Module) before the SPPF of Backbone of the YOLOv5s model, and uses ACON(Acrivate or Not) to replace ReLU as the activation function of the network, which improves the detection accuracy and can be effectively applied to vehicle detection in complex scenes.

Citation Download Citation

Cuihua Tian, Yawei Chen, Zhihui Li, and Minghui Fan "Improved vehicle detection based on YOLOv5s", Proc. SPIE 12626, International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022), 126261C (28 April 2023); https://doi.org/10.1117/12.2674439

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