Analysis of Abnormal Behavior Detection in Electric Power Construction Sites Based on Improved YOLOv7
This paper expounds a method for detecting abnormal behavior of personnel in power construction sites based on the improved YOLOv7 algorithm.The method adds SE attention mechanism in the head area to enhance feature extraction ability,while adding a small target dedicated detection head to improve recognition ability in large scenes and small target environments.The experimental results show that the improved model improves accuracy by 0.7％and mAP by 2.1％,reaching 94.5％,thereby achieving real-time detection of abnormal behavior,especially small targets,in power construction sites.
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