The tension clamp plays the role of connecting wires and carrying current in the power line, and its crimping quality is directly related to the safety of the power grid. In order to solve the problems of complex operation and high personnel requirements in the DR defect detection of tension clamp crimping, an EW+YOLOv8 application EW+YOLOv8 tension clamp DR image defect detection and identification scheme was proposed, which selected YOLOv8n with good accuracy and good real-time performance as the detection reference network, and then added the efficient channel attention mechanism ECA to highlight the key information of the defect feature map, and applied the Wise-IoU loss function based on the dynamic non-monotonic focusing mechanism to replace the CIoU loss function. Reduce the impact of low-quality anchor frames in labeled samples. Based on the data set preparation and the analysis of test evaluation indexes, relevant ablation experiments and comparative experiments were carried out, which showed that EW+YOLOv8n had fast calculation speed and few model parameters when ensuring high detection accuracy, and was applied to the detection and identification of power tension clamp crimping defects with mAP@0.5 and FPS of 97.4% and 50 sheets, respectively, which could meet the actual detection needs of the project.