DEFECTCNN: Improved Discriminative Convolution Neural Network Towards Instantaneous Automatic Detection and Classification of Complex Defect in Fabrics

                  Due to enormous growth of textile industries has increased demand for the automatic fabric defect detection and classification system to the fabric material as it plays a crucial role in maintaining the quality of the services. Machine learning model has employed as automatic defect detection system to identify the material quality. Despite of several advantageous of the machine learning model, those models faces several challenges on handling the complex and uncertainty of varied texture and structural patterns. Further it is complex to process the boundaries and features with high degree of intra class variation and low degree of interclass variations. On leveraging and exploiting the deep learning architecture, the over lapping and varied texture patterns can be efficiently discriminated on defects. In this paper, a new deep learning architecture entitled as discriminative convolution neural model is proposed to detect and classify the defects in the fabric materials into various defect classes. Initially fabric image preprocessed on basis of the noise filtering through wiener filter and image enhancement through CLAHE technique. Enhanced image is segmented using image thresholding technique to segment it into the various regions on basis of pixel information’s by grouping the neighbouring similar pixels intensity or textures to represent a mask. Segmented image regions are projected to the convolution neural network. Convolution layer of network is to extract the features from its composition containing kernels with different weights. It computes the high level features for different pixels based on surrounding and neighbouring pixel values on striding to produce the feature map containing gradient and edge of the images.  ReLU activation function is applied to reduce the non linearity among the features in the feature map. Pooling layer of the model down-sample the convolved features to produce the activation map. Activation map is obtained using max pooling as it returns maximum value from the segment of the image processed using kernels. Activation map is transformed into tabular structure to perform the classification easily. In addition drop out layer is incorporated in the model to eliminate the overfitting issue during classification on reducing the correlation among the neurons. Fully connected layers of the model is used to learn the flattened features with weights and bias to classify the flatten features using softmax layer on basis of defect classes such as Hole , Color Spot, Thread Error  and foreign body.  Experimental analysis of the proposed architecture is carried out on TILDA dataset using cross fold validation to analyse the representation ability to discriminate the features with large variance between the different classes. From the results, it is confirming that proposed architecture exhibiting higher performance in classification accuracy of 98.43% in classifying the fabric defect on compared with conventional approache

Defect Detection for Patterned Fabric Images Based on GHOG and Low-Rank Decomposition

In contrast to defect-free fabric images with macro-homogeneous textures and regular patterns, the fabric images with the defect are characterized by the defect regions that are salient and sparse among the redundant background. Therefore, as an effective tool for separating an image into a redundant part (the background) and sparse part (the defect), the low-rank decomposition model provides an ideal solution for patterned fabric defect detection. In this paper, a novel patterned method for fabric defect detection is proposed based on a novel texture descriptor and the low-rank decomposition model. First, an efficient second-order orientation-aware descriptor, denoted as GHOG, is designed by combining Gabor and histogram of oriented gradient (HOG). In addition, a spatial pooling strategy based on human vision mechanism is utilized to further improve the discrimination ability of the proposed descriptor. The proposed texture descriptor can make the defect-free image blocks lay in a low-rank subspace, while the defective image blocks have deviated from this subspace. Then, a constructed low-rank decomposition model divides the feature matrix generated from all the image blocks into a low-rank part, which represents the defect-free background, and a sparse part, which represents sparse defects. In addition, a non-convex log det as a smooth surrogate function is utilized to improve the efficiency of the constructed low-rank model. Finally, the defects are localized by segmenting the saliency map generated by the sparse matrix. The qualitative results and quantitative evaluation results demonstrate that the proposed method improves the detection accuracy and self-adaptivity comparing with the state-of-the-art methods