改进SAM的皮革缺陷分割与自适应裁切方法OA

To address the issues of leather surface defect localization and CAD path deviations caused by flexible cutting piece deformation,we propose an improved SAM-based method for leather defect segmentation and adaptive cropping.This method constructs Leather-SAM based on the SAM-Med2D ViT-B image encoder and a channel-spatial dual-branch adapter,and combines ORB feature matching,global affine transformation matrix,and multi-beam cutting safety control to build a defect recognition and cutting optimization algorithm based on Leather-SAM.Experimental results show that the method achieves IoU,Dice,and contour Fl scores of 54.58％,65.22％,and 88.08％,respectively,with an inference speed of 12.2 f/s,providing a reproducible technical pipeline for intelligent leather layout and cutting.