A Novel Framework For Classifying Leather Surface Defects Based On A Parameter Optimized Residual Network

Existing works on leather surface defects are mainly focused on the defect detection without performing the classification automatically. On the other hand, the manual classification has the disadvantages including a misjudgment, the occurrence of cumbersome labors and a high cost. To address these issues, this paper proposes a framework for classifying the leather surface defects based on a parameter optimized residual network. In this framework, an ultra high definition imaging system is first utilized to obtain the images of the leather surface. Then, two important network parameters are optimized. They are the size of the image data set and the size of the sliding patch window. Here, the size of the image data set is determined by achieving the tradeoffs between the evaluated workload and the classification accuracy. On the other hand, the size of the sliding patch window is obtained by the least squares method. In order to mitigate the limitation of the gradient disappearance in a deep network, a residual module is introduced to the proposed framework. Finally, the defect classification is performed based on the extracted features using multi-layer convolution and pooling operations. Computer numerical simulation results show that the leather surface defects can be classified effectively by the proposed framework. Also, the classification accuracy can reach 94.6%.