Detection and Location of Safety Protective Wear in Power Substation Operation Using Wear-Enhanced YOLOv3 Algorithm

Abstract

Wearing personal safety protective equipment (PSPE) plays a key role in reducing electrical injuries to electrical workers. However, substation employees often ignore this regulation due to lack of safety awareness and discomfortable feeling of wearing PSPE. Therefore, it is necessary to develop a detection algorithm for PSPE and workers to build real-time video surveillance systems in power substations. In this paper, a wear-enhanced YOLOv3 method for real-time detection of PSPE and substation workers is proposed. The gamma correction is first applied as the preprocessing method to highlight the details of the operators and data augmentation is performed. Next, K-means++ algorithm replaces K-means in wear-enhanced YOLOv3 method to derive the most suitable prior bounding box size and lift the detection speed. Then, the proposed method can be quickly and effectively trained based on transfer learning. Finally, extensive experiments are carried out on a dataset of images about usage of safety helmets, insulating gloves and boots. Using the proposed method, the mean average precision is improved by over 2% and the frames per second is the highest compared with other typical object detection methods, which illustrates the effectiveness of the wear-enhanced YOLOv3 method for PSPE and workers detection.

Keywords

• Objects detection
• deep learning
• YOLOv3
• power substation
• safety helmet