Skeleton-Based Violation Action Recognition Method for Safety Supervision in Operation Field of Distribution Network Based on Graph Convolutional Network

Safety accidents in the operation field of the distribution network often occur, which seriously endanger the safety and lives of operators. Existing identification methods for safety risk can identify static safety risks, such as no-helmet, no-safety gloves, etc., but fail to identify risks in the dynamic actions of operators. Therefore, this paper proposes a skeleton-based violation action-recognition method for supervision of safety during operations in a distribution network, i.e., based on spatial temporal graph convolutional network (STGCN) and key joint attention module (KJAM), which can implement dynamic violation behavior recognition of operators. In this method, the human posture estimation method, i.e. Multi-Person Pose Estimation, is utilized to extract the skeleton information of operators during operations, and to construct an undirected graph, which reflects the movement and posture of the human body. Then, the STGCN is utilized to identify actions of operators that can lead to dynamic violations. In addition, the KJAM captures important joint information of operators. The effectiveness and superiority of the proposed method are verified in comparison to other action recognition methods. The experimental results show that the proposed method has higher recognition accuracy for common violations collected at the actual operation site of the distribution network and shows a strong generalization ability, which can be applied to the video monitoring system of field operations to reduce the occurrence of safety accidents.