The thermal insulation

waterproofing

isolation

vapor barrier

and leveling layers are critical structural and functional components of substation rooftops.Defects on these layers can significantly impact the performance

lifespan

and personnel safety of substations.This study proposes an improved target detection solution for these surface defects using a you only look once

YOLOv5s algorithm enhanced with the optimal transport assignment(OTA).The OTA algorithm refines label assignment

providing a more accurate match than traditional threshold methods and balancing the learning between positive and negative samples.Experimental results demonstrate that the OTA-optimized YOLOv5s algorithm can comprehensively utilize image information and learn geometric features

thereby reducing localization loss

object loss

and classification loss.Furthermore

the OTA enhances precision

recall

and mean average precision(MAP)

indicating improved predictive accuracy

integrity

and overall performance of the model.Therefore

the application of the YOLOv5s algorithm combined with OTA optimization for rooftop defect detection in substations holds significant practical value.