Pedestrian target detection algorithm based on improved YOLO v5

Gaojun Zou; Zhian Zhang

doi:10.1117/12.3013616

29 November 2023 Pedestrian target detection algorithm based on improved YOLO v5

Gaojun Zou, Zhian Zhang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 1293719 (2023) https://doi.org/10.1117/12.3013616
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

In order to solve the problem of low accuracy of pedestrian target detection and recognition caused by occlusion between people when people are crowded with each other, an improved method of introducing SENet channel attention mechanism based on YOLOV5 neural network was proposed. By introducing SENet channel attention mechanism to further explore the relationship between the feature channels in the image, the feature extraction of the human target area is enhanced, and the accuracy rate of human target detection and recognition is well improved. Compared with the general YOLO V5 algorithm, the improved algorithm introduced by SENet attention mechanism proposed in this paper has a lower missed rate of target detection and a higher correct rate of target detection and recognition when people are crowded with each other, and also has the real-time performance of the general YOLO V5 algorithm.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gaojun Zou and Zhian Zhang "Pedestrian target detection algorithm based on improved YOLO v5", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 1293719 (29 November 2023); https://doi.org/10.1117/12.3013616

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