基于改进SAM模型的多任务轨道缺陷检测方法OA北大核心CSTPCD

Track defect detection is a critical task for ensuring the safe operation and maintenance of rail transportation.Existing machine vision-based detection methods mainly focus on segmentation of railway images,which have high model time complexity,serious background noise interference and poor segmentation effects.This paper proposes an improved multi-task image segmentation model based on Segment Anything Model(Multi-Task Advanced SAM,MASAM)to effectively improve training efficiency and defect segmentation accuracy.First,the defect scope is determined and the boundary coordinates are obtained through the target detection module;then the boundary coordinates are converted into sparse embeddings;finally,the sparse embeddings and the image feature vectors processed by the Image Encoder module in the SAM model are inputted to the Mask Decoder part to obtain the defect mask prediction result.The experimental results show that the prediction efficiency and accuracy of the MASAM model are superior to other models in multi-task railway defect detection.