Automatic Fabric Defect Detection Using Cascaded Mixed Feature Pyramid with Guided Localization Generic object detection algorithms for natural images have been proven to have excellent performance. In this paper, fabric defect detection on optical image datasets is systematically studied. In contrast to generic datasets, defect images are multiscale, noisefilled, and blurred. Backlight intensity would also be sensitive for visual perception. Largescale fabric defect datasets are collected, selected, and employed to fulfill the requirements of detection in industrial practice in order to address these imbalanced issues. An improved twostage defect detector is constructed for achieving better generalization. Stacked feature pyramid networks are set up to aggregate crossscale defect patterns on interpolating mixed depthwise block in stage one. By sharing feature maps, centerness and shape branches merges cascaded modules with deformable convolution to filter and refine the proposed guided anchors. After balanced sampling, the proposals are downsampled by positionsensitive pooling for region of interest, in order to characterize interactions among fabric defect images in stage two. The experiments show that the endtoend architecture improves the occluded defect performance of regionbased object detectors as compared with the current detectors.

Fabric Defect Detection Method Based on Sparse and Dense Mixed Lowrank Decomposition On account of the issue that there is severe noise in the detection of defects by the traditional lowrank decomposition defect detection method, in this paper, we present an efficient fabric defect detection approach which utilizes the sparse and dense decomposition on the base of lowrank representation. Firstly, the fabric image is uniformly segmented into image blocks. Each image block is spanned into a column vector, which is assembled to constitute the fabric image feature matrix. Then, the sparse and dense mixed lowrank decomposition model is constructed with the introduction of the F norm. The presented model is optimized by alternating direction multiplier method (ADMM) and augmented Lagrange multiplier (ALM), and the low rank array, dense matrix and sparse array are obtained. Finally, a thresholding segmentation approach is employed to detect the defect area by partitioning the salience map. Experimental results demonstrate that the proposed method achieves an efficient detection property, and it is superior to the current approaches.