Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 72, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2023.3280519
Keywords
Deep learning; defect detection; multiscale context aggregation; textile fabric
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This article presents an advanced fabric defect detection model based on deep learning algorithm. The model incorporates a parallel dilated attention module (PDAM) and a hook-shaped feature pyramid network (HookFPN), which leads to improved detection efficiency without sacrificing accuracy.
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 tradeoff between detection efficiency, accuracy, and generalizability. This article presents an advanced fabric defect detection model based on a deep learning algorithm. A novel parallel dilated attention module (PDAM) was designed: this is nested in the deep layers of the neural network backbone to establish global channel dependencies and capture multiscale contextual information. Furthermore, a hook-shaped feature pyramid network (HookFPN) 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 $\alpha $ . 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.
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