Abstract: With the rapid economic development, the government has increased investment in municipal construction, which usually takes a long time, involves many open-air operations, and is affected by cross-construction, traffic, climate and environment, and so on. The safety protection of urban construction workers has been a concern. In this paper, an improved algorithm based on YOLOv5s for the simultaneous detection of helmets and reflective vests is proposed for municipal construction management. First, a new data enhancement method, Mosaic-6, is used to improve the model's ability to learn local features. Second, the SE attention mechanism is introduced in the focus module to expand the perceptual field, strengthen the degree of association between channel information and the detection target, and improve the detection accuracy. Finally, the features of small-scale targets are interacted and fused in multiple dimensions according to the Swin transformer network structure. The experimental results show that the improved algorithm achieves accuracy, recall, and mean accuracy rates of 98.5%, 97.0%, and 92.7%, respectively. These results show an average improvement of 3.4 percentage points in mean accuracy compared to the basic YOLOv5s. This study provides valuable insights for further research in the area of urban engineering security and protection.

Shuangyuan Li, Yanchang Lv, Mengfan Li and Zhengwei Wang. “Detection of Protective Apparatus for Municipal Engineering Construction Personnel Based on Improved YOLOv5s”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.7 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140741

@article{Li2023,
title = {Detection of Protective Apparatus for Municipal Engineering Construction Personnel Based on Improved YOLOv5s},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140741},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140741},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Shuangyuan Li and Yanchang Lv and Mengfan Li and Zhengwei Wang}
}