Optimization of target detection algorithm based on Yolov5

Jing Jing Geng; Heng Wei Kou; Xue Fang Zhang

doi:10.1117/12.2678940

25 May 2023 Optimization of target detection algorithm based on Yolov5

Jing Jing Geng, Heng Wei Kou, Xue Fang Zhang

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120V (2023) https://doi.org/10.1117/12.2678940
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

In order to improve the target detection performance of the detection model pair in the target tracking scenario, this paper abandons the use of the CSPDarknet53 backbone feature extraction network in the original algorithm and selects the lightweight network MobileNetV3-large as the new backbone feature extraction network. And the channel attention SE module in the original inverted residual structure of the network is changed to the integrated attention module based on the channel dimension and spatial dimension. According to the training results on the dataset CrowdHuman, compared with the original Yolov5 model, the improved detection algorithm improves the detection accuracy by 1.5% and the detection speed by 10.2 frames per second, which is more in line with the real-time requirements of the automatic driving scene.

Citation Download Citation

Jing Jing Geng, Heng Wei Kou, and Xue Fang Zhang "Optimization of target detection algorithm based on Yolov5", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120V (25 May 2023); https://doi.org/10.1117/12.2678940

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KEYWORDS

Detection and tracking algorithms

Target detection

Data modeling

Education and training

Mathematical optimization

Convolution

Performance modeling

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