The tension clamp plays the role of connecting wires and carrying current in the transmission line, and its crimping quality is directly related to the safe and effective operation 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, a DR image defect evaluation method using VA-UNet segmentation technology was proposed. Firstly, the semantic segmentation model VA-UNet for DR image defects in tension clamps is studied, VGG16 with significant image feature extraction and analysis ability is selected as the backbone network, multi-scale feature fusion is enhanced by integrating spatial pyramid pooling structure ASPP, and mixed loss function is introduced to accelerate the model convergence and improve the segmentation accuracy. Then, a grading method combining the model prediction segmentation results and related quantitative analysis was studied to realize the hazard severity assessment of DR defects in tension clamp crimping, which provided a reference for the subsequent wire clamp treatment. Based on the data set preparation and the analysis of test evaluation indicators, the relevant ablation experiments showed that the mIoU and mPA of VA-UNet reached 84.14% and 91.58%, respectively, which were significantly higher than those of the original model. The experiment of assessing the severity of DR defects in tension clamp crimping shows that the method is scientific and practical.