Focus Generator with Score Classification on Fabric Defect Detection Fabric defect detection plays an important role in the production of fabrics. Thanks to deep learning and largescale datasets, popular object detection tasks have made great progress. RetinaNet has been widely used in object detection tasks in various fields, such as face detection, due to its flexibility and operability. Itu0027s high accuracy and detection results are derived from datasets with rich features. So limited by the smallscale fabric defect dataset, RetinaNet is difficult to apply to fabric defect detection task. In this paper, we propose an effective neural network approach to solve the problem of smallscale fabric dataset and apply RetinaNet to this task. To overcome the insufficient features because of the smallscale dataset, we first propose a generative model to add Gaussian noise on latent space, called focus generator, which can be controlled with defect instances to generate more data. Then we add a classification model to limit influence produced by the focus generator, called score classification. Finally, we merge the focus generator and score classification with an improved RetinaNet to achieve fabric defect detection, therefore, we name our model FSR. By the way, the operations of adding noise are different on the steps of training and testing. The experimental result shows that our proposed method can achieve better performance comparing to our baseline RetinaNet and finally achieve accuracy of 83.4% on our smallscale fabric defect dataset.

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.