Insulators are an important part of transmission lines, and their discharge problem is one of the main causes of transmission line faults, so there is a need for algorithms that can accurately and quickly assess the severity of insulator discharge and can be monitored in real time at the edge. In this paper, in order to address the above problems, the YOLOv8 target detection algorithm is firstly lightweighted and improved. Firstly, Mosaic-9 data enhancement method is introduced to improve the input, which improves the robustness and generalization ability of the algorithm; then GhostNet network is introduced to replace the backbone network, which realizes the lightweighting of the model; then the GeLU activation function is introduced to replace the ReLU activation function, which improves the convergence speed and detection accuracy of the algorithm; then the GELU activation function is introduced to replace the ReLU activation function. The GeLU activation function is introduced to replace the RELU activation function to improve the convergence speed and detection accuracy of the algorithm; finally, the SIoU loss function is introduced to optimize the network, and the UDD-YOLO edge-end insulator discharge severity assessment algorithm is finally formed. Experimentally verified, it achieves 87.6% mAP and 58 frames/s inference speed in the edge-end device, which meets the requirement of evaluating the severity of insulator discharge in the edge-end, and the effectiveness and superiority of the algorithm proposed in this paper is proved by ablation and comparison tests.