Paper
10 August 2023 Occluded pedestrian detection based on improved YOLOv5n
Qiuxing Zhang, Fanghua Yang, Qikai Zhou, Wei Zhang, Ruizhi Li
Author Affiliations +
Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127483X (2023) https://doi.org/10.1117/12.2689369
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China
Abstract
Aiming at the problem of pedestrian targets occlusion and multi-scale error and missed detection in pedestrian detection, a lightweight pedestrian detection algorithm based on improved EA-YOLOv5n is proposed. This method introduces the ECA attention module into the backbone feature extraction network, and learns the channels of pedestrian images by learning Information, improve the accuracy of pedestrian object detection in the case of occlusion, improve the calculation method of Bounding box loss function for the disadvantages of loss function calculation, adopt EIoU Loss and introduce power transformation to obtain higher bounding box regression accuracy. The experimental results show that using the improved model to conduct experiments on the Widerperson dataset reaches 69.6% mAP, which is 2.0% higher than the original algorithm, and the detection speed reaches 65FPS.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qiuxing Zhang, Fanghua Yang, Qikai Zhou, Wei Zhang, and Ruizhi Li "Occluded pedestrian detection based on improved YOLOv5n", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127483X (10 August 2023); https://doi.org/10.1117/12.2689369
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KEYWORDS
Object detection

Detection and tracking algorithms

Convolution

Data modeling

Performance modeling

Education and training

Feature extraction

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