Through literature review and case analysis,the paper explores the technology of AI-based automatic defect detection and grading in leather.It focuses on addressing the challenges faced by manual inspectors of Du'an goat leather,where factors such as weather conditions,variations in lighting,and visual impairments often lead to high error rates in defect marking.The study argues that artificial intelligence techniques such as Fully Convolutional Networks(FCN)in deep learning(DL)and Deep Neural Networks(DNN)in machine learning(ML)can effectively automate the detection and grading of various defects in Du'an goat leather.This approach maximizes compliance with the quality standards required under the China-ASEAN RCEP(Regional Comprehensive Economic Partnership)for leather imports and exports among member countries.
维普
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