Affiliation:
1. School of Electronic, Electrical Engineering and Physics Fujian University of Technology Fuzhou 350118 China
2. Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou 350011 China
3. Institute of Intelligent Ocean and Engineering Fujian University of Technology Fuzhou 350108 China
Abstract
Electrical workers must wear insulating gloves during daily maintenance. Detecting the condition of these gloves is significant for power safety. Due to the limitations of existing detection algorithms in accuracy and lightweight on power construction sites, this paper proposes a Faster‐YOLOv8 algorithm to detect the wearing condition of insulating gloves. First, it replaces the Bottleneck in the C2f module with the Faster Block and introduces a new C2Faster module. This reduces model parameters while improving performance. Second, the feature pyramid is improved to enhance the combination of deep and shallow semantic information. This improvement aims to improve the network's focus on detecting small targets. Finally, the GSConv is introduced into the neck network to reduce the model parameters and calculation. Additionally, GSConv enhances feature information exchange through shuffle operation. The effectiveness of the Faster‐YOLOv8 algorithm is verified through comparisons with mainstream algorithms such as the Faster‐RCNN algorithm, SSD algorithm, YOLOv5 algorithm, and YOLOv7‐tiny algorithm. The results demonstrate that the Faster‐YOLOv8 algorithm outperforms the above algorithms in terms of detection accuracy. Compared with the original YOLOv8 network model, the mAP is improved by 3.8%, the mAP of the wrong glove increased by 9.8%, the model size is reduced by 33.3%, and the detection speed can reach 81 FPS. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.