This invention provides a YOLO-based method for identifying fault types in 10kV distribution network lines, comprising: constructing a fault image dataset and data augmentation; constructing an improved YOLOv8 fault identification
model architecture; constructing a multi-task
loss function for fault identification; training the line fault identification model;
inference detection based on line inspection images; post-
processing optimization of fault detection results; and outputting identification results. This invention achieves high-precision, robust, and real-time intelligent identification and classification of various types of line faults in 10kV
distribution networks, effectively improving the efficiency of intelligent inspections and the timeliness of fault early warnings, and ensuring the safe and stable operation of the distribution network.