Freeway vehicle detection on improved YOLOv7

Shaojie Gong; Kun Mao; Haifeng Yuan; Yongjie Zheng; Shu Zhang; Qiang Zhou; Lu Zou; Zhen Huang

doi:10.1117/12.2684189

1 August 2023 Freeway vehicle detection on improved YOLOv7

Shaojie Gong, Kun Mao, Haifeng Yuan, Yongjie Zheng, Shu Zhang, Qiang Zhou, Lu Zou, Zhen Huang

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 127541Q (2023) https://doi.org/10.1117/12.2684189
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Vehicle detection is the important basis for smart traffic. In order to further improve the performance of vehicle detection in freeway scenes, an improved YOLOv7 network is proposed in this paper. Firstly, vehicle dataset is created via cameras mounted on freeway. Secondly, Improved data augmentation method is applied to simulated the special environment interference. Then, SimAM attention mechanism module is inserted into YOLOv7, and the optimal adding position is obtained by discussing the performance of different attention composition. Finally, we provide experimental results to verify the effectiveness of the improvement. The results show that method proposed in this paper increase the mAP by 11% than original YOLOv7 on our dataset.

Citation Download Citation

Shaojie Gong, Kun Mao, Haifeng Yuan, Yongjie Zheng, Shu Zhang, Qiang Zhou, Lu Zou, and Zhen Huang "Freeway vehicle detection on improved YOLOv7", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 127541Q (1 August 2023); https://doi.org/10.1117/12.2684189

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KEYWORDS

Object detection

Head

Network architectures

Feature extraction

Detection and tracking algorithms

Evolutionary algorithms

Neurons

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