In the textile industry, surface defects can greatly damage the value of fabric, but the coexistence of subtle defects and elongated defects poses a significant challenge to the localization of them. Existing convolutional neural networks-deep learning methods, especially the YOLO series, can present promising fabric defect detection. However, their performance is limited in simultaneously learning local and global features, leading to inaccurate localization results. To address this issue, this paper proposes a Fabric Defect Detection Network (FDDNet) based on Spatial Depth-Transforming Convolution (SDTC) and Multiscale Dilated Self-attention Fusion Module (MDSFM). Firstly, to enhance the local feature characterization capability of the backbone network, our FDDNet proposes spatial depth-transforming convolutions to preserve more fine-grained information. Subsequently, to effectively integrate global and local information and enhance global-local modeling capability, the multiscale dilated self-attention fusion module is introduced by combining self-attention mechanisms and dilated convolutions, thus enabling the model to percept scale changes and achieving multi-scale defect localization. Experiment results conducted on the publicly available Tianchi fabric dataset and a self-made denim dataset show that, the proposed FDDNet can achieve the AP50 of 54% and 56.8% respectively, which outperforms mainstream state-of-the-art methods.
Huang et al. (Sat,) studied this question.