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 large-scale 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. It's high accuracy and detection results are derived from datasets with rich features. So limited by the small-scale 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 small-scale fabric dataset and apply RetinaNet to this task. To overcome the insufficient features because of the small-scale 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 small-scale fabric defect dataset.