Abstract
The power site is a high-risk workplace, and illegal operations will bring serious threats to the safety of relevant personnel. In order to improve the inefficiency of traditional manual supervision methods, this paper proposes to identify illegal operations based on an improved deep learning model for specific power operation scenarios. The model combines the YOLOv3 algorithm and the Self-Attention mechanism for target detection; then integrates the scene recognition mechanism at the same time, uses the intersection and ratio to set the logic judgment function to detect the illegal operation behavior of power operations in a specific scene. The simulation shows that the model has an accuracy rate of 94.6% in identifying illegal wearing problems in specific work scenarios. Compared with basic YOLOv3, model shown in paper has high precision and meets engineering needs. In addition, the algorithm shown in this paper is suitable for the identification of violations in complex electrical work environments.
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