HookNet: Efficient Multiscale Context Aggregation for High-Accuracy Detection of Fabric Defects
Zhong Xiang, Yujia Shen, Miao Ma, Miao Qian
Abstract
Automatic defect detection is a critical stage of quality control in the textile industry. Because defects vary in texture structure, size, and spatial distribution, existing defect detection methods have difficulty in achieving a perfect trade-off between detection efficiency, accuracy, and generalizability. This paper presents an advanced fabric defect detection model based on a deep learning algorithm. A novel parallel dilated attention module was designed: this is nested in the deep layers of the neural network backbone to establish global channel dependencies and capture multiscale contextual information. Further, a hook-shaped feature pyramid network was developed for multiscale context aggregation: this directs the network to focus on lower-level features and is helpful for further improving detection efficiency without sacrificing detection accuracy. In addition, the Alpha-GIoU loss is used to improve the accuracy of bounding box regression because of its modulated power parameter α. The advantages and effectiveness of the proposed method were statistically analyzed using data from several public datasets. The code is available at https://github.com/aabb605/HookNet.