Leather　defects　with　variable　morphology　and　high　local　similarity　are　of　difficulty　in　extracting　features　comprehensively　and　accurately.　In　this　work,　a　refined　surface　defect　segmentation　method　based　on　improved　U-Net　network　is　proposed.　On　the　encoder　side,　a　cascaded　dilated　convolution　module　is　embedded　to　obtain　the　global　features　while　preserving　the　detail　information　of　the　original　image,　and　a　feature　fusion　module　is　added　to　the　jump　connection　to　reduce　local　features　loss　caused　by　directly　splicing　of　the　high-level　and　low-level　feature　tensor;　on　the　decoder　side,　a　decoding　module　based　on　the　channel　attention　mechanism,　which　can　guide　the　network　to　adaptively　focus　on　defective　regions,　is　used　to　replace　the　original　convolutional　layer;　to　further　integrate　high-level　information,　a　global　average　pooling　module　is　embedded　as　the　semantic　guide　to　improve　the　discrimination　capability　of　the　network　from　similar　defects　at　the　decoding　end.　The　experimental　results　conducted　on　a　leather　dataset　containing　7　kinds　of　defects　show　that　the　proposed　method　achieves　99.17%,　93.27%,　98.39%,　and　88.88%　in　PA,　MPA,　FWIoU,　and　MIoU,　which　is　0.28,　2.78,　0.53,　and　4.03　percentage　points　better　than　that　of　U-Net.　The　qualitative　and　quantitative　analysis　results　demonstrate　that　the　algorithm　proposed　has　remarkable　ability　to　refine　the　segmentation　in　leather　defect　recognition.　©　2024　Institute　of　Computing　Technology.　All　rights　reserved.