There are problems in existing object detection systems caused by the complicated detection environment of electric power construction, large variance of detected object shape and poor recognition of visual features. This paper proposes an object detection model for illegal wearing detection in electric power construction. First, an asymmetric convolutional group is added to the feature extraction backbone network of YOLOv5 to obtain more discriminative and robust visual features. Second, in order to be able to adaptively focus on feature regions that are more relevant to the detected object under the influence of visual noise, a transformer-based global attention mechanism for modeling contextual information is used to improve the efficiency and accuracy of visual information processing. Finally, the effectiveness and superiority of the improved YOLOv5 algorithm in this paper are demonstrated by comparing with existing object detection algorithms. Meanwhile, the effectiveness of the improved modules in the target detection model is demonstrated by ablation experiments.