This invention relates to the field of
power grid operation safety technology, and discloses a method and
system for intelligent identification of
transmission line outer sheath damage based on
deep learning. The method includes the following steps: acquiring original images and BeiDou coordinates, completing
timestamp alignment and basic preprocessing to obtain an enhanced image as the
inference input; using a
unified model to perform multi-task
inference on the enhanced image, outputting a multi-instance
inference set, and performing temperature calibration on the classification confidence, calculating the
centroid / box center, normalizing the severity, and measuring uncertainty; the multi-instance inference set includes bounding boxes, segmentation masks, severity scores, classification confidence, and uncertainty. This invention can suppress the
impact of positioning errors on identification and early warning in scenarios with obstruction and multipath conditions such as valleys, urban canyons, and
metal structure reflections, reducing the risks of missed detections, false detections, and delayed handling, improving stability and robustness in complex weather and long corridor scenarios, and achieving all-weather, low-latency, and traceable on-
site management.