Fabric Surface Defect Detection Using SE-SSDNet

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For fabric defect detection, the crucial issue is that large defects can be detected but not small ones, and vice versa, and this symmetric contradiction cannot be solved by a single method, especially for colored fabrics. In this paper, we propose a method based on a combination of two networks, SE and SSD, namely the SE-SSD Net method. The model is based on the SSD network and adds the SE module for squeezing and the Excitation module after its convolution operation, which is used to increase the weight of the model for the feature channels containing defect information while re-preserving the original network to extract feature maps of different scales for detection. The global features are then subjected to the Excitation operation to obtain the weights of different channels, which are multiplied by the original features to form the final features so that the model can pay more attention to the channel features with a large amount of information. In this way, large-scale feature maps can be used to detect small defects, while small-scale feature maps are used to detect relatively large defects, thus solving the asymmetry problem in detection. The experimental results show that our proposed algorithm can detect six different defects in colored fabrics, which basically meets the practical needs.