Academic Journal of Computing & Information Science, 2024, 7(1); doi: 10.25236/AJCIS.2024.070102.
Liuyang Gao, Yun Yang
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, China
With the rapid development of artificial intelligence technology, pedestrian detection tasks have been widely used in intelligent security and other fields. In rainy and snowy weather, pedestrian detection models may be severely affected, resulting in missed detections and false alarms. This article proposes a lightweight pedestrian detection method based on improved YOLOv5 for rainy and snowy weather conditions. Firstly, to address the issue of insufficient datasets, artificial rain and snow noise is added to the collected pedestrian data. This increases the diversity of the data and improves the robustness of the model in handling pedestrian detection tasks in rainy and snowy weather. Next, a lightweight pedestrian detection model called MAC_YOLOv5s is designed based on the YOLOv5 network model. The MAC_YOLOv5s model replaces the backbone network of the original network with the Mobilenetv3 network to achieve light weight. In addition, the CBAM attention mechanism is integrated to enhance the model's anti-interference ability. The progressive feature pyramid network AFPN is introduced to fuse feature information between adjacent layers, extracting more important features. Experimental results show that the improved MAC_YOLOv5s lightweight pedestrian detection network model reduces the model size to one-third of the original YOLOv5s model, with a file size of only 5.35MB. The mAP value reaches 0.683. These results indicate that the improved MAC_YOLOv5s model has fewer parameters and a smaller model size compared to the original model, making it suitable for pedestrian detection tasks in rainy and snowy weather conditions.
Pedestrian Detection, YOLOv5s network, Mobilenetv3 Network, Attention Mechanism
Liuyang Gao, Yun Yang. A Method of Lightweight Pedestrian Detection in Rainy and Snowy Weather Based on Improved YOLOv5. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 1: 9-17. https://doi.org/10.25236/AJCIS.2024.070102.
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