Leather products are widely used across various fields,permeating every aspect of daily life.However,during the production of synthetic leather fabrics,defects are inevitable,directly affecting the quality and price of leather products.Early identification of these defects in the production process can prevent further production losses.Nevertheless,the high local similarity of defects on leather fabric surfaces causes significant similarities between different types of defects,leading to poor detection results.To address this issue,the article proposed an end-to-end defect detection method for leather fabric surfaces,achieving finer granularity in distinguishing leather defects.To address the high similarity between defect classes,this paper introduced an adaptive convolutional attention(AC A)module.This module comprises channel attention and spatial attention,integrating the channel and spatial attention information through a residual structure to generate more discriminative features.Two different-sized convolutional kernels in the spatial attention work in concert to effectively enhance the differences between defects,focusing attention on target features and thus reducing the interference response to background information.To amplify the differences between leather fabric defect classes,ACA was incorporated into the backbone network to enhance the semantic feature representation capabilities.This integration not only improves the network's ability to differentiate between defect types but also ensures more accurate detection outcomes.Then,the article designed a feature pyramid network based on adaptive convolutional attention(AC-FPN)to improve multi-scale fusion.By leveraging the feature information enhanced by ACA,the network enables the flow of information between different scales,allowing for finer differentiation between defects and background.Such enhancement significantly improves the detection capability of defects at different scales,achieving finer granularity in leather defect differentiation.The multi-scale fusion process ensures that defects of various sizes and shapes are accurately detected,regardless of their scale,contributing to a more robust detection system.Finally,the traditional detection head was replaced with the side-aware boundary localization(SABL)detection head,enabling precise localization of leather fabric defects.The SABL detection head is specifically designed to enhance the accuracy of defect localization by focusing on the boundaries of defects,ensuring that even the smallest and most subtle defects are accurately identified and localized.This replacement is crucial for improving the overall precision of the defect detection system,making it more reliable for practical applications in leather fabric production.The article validated the proposed method using a self-constructed leather fabric dataset and compared it with different methods.Experimental results demonstrate that the proposed method achieves better performance in distinguishing between different defect types with similar appearances.Compared to other methods,this method exhibits superior detection accuracy across various defect types,with AP,AP50,and AP75 evaluation metrics reaching 83.4,89.7,and 85.6,respectively.This provides a new perspective for automated surface defect detection of leather fabrics.The improved accuracy metrics indicate that the proposed method is highly effective in identifying and classifying defects,with significant improvement over existing methods.The proposed defect detection method for leather fabrics demonstrates better performance compared to other methods,offering new feasibility for defect detection.Despite the advantages mentioned above,the use of a dataset primarily comprising grayscale images may reduce the ability to extract effective information for some colored leather defects.In future research,color cameras can be used to capture images of leather defects and incorporate color information to distinguish some leather fabric surface defects.Additionally,exploring advanced image processing techniques and integrating them with the current approach could further enhance the defect detection capabilities,so as to make the system more versatile and applicable to a wider range of leather products.
维普
万方数据
