Lightweight Single Shot Multi-Box Detector: A fabric defect detection algorithm incorporating parallel dilated convolution and dual channel attention

Abstract

For the textile industry, fabric defect detection is an important part of production. In order to make the automatic fabric defect detection system used in production sites, this article proposes a lightweight algorithm Lightweight Single Shot Multi-Box Detector (LW-SSD) to address the issues of low detection accuracy, high computational complexity, and difficulty in deploying on hardware devices with limited computing power in fabric defect detection. Firstly, MobileNetv3 is introduced as the backbone network to reduce the number of model parameters. Secondly, in the feature fusion module, down-sampling stacking is used to fuse the feature maps processed by maximum pooling and regular 3 × 3 convolution, respectively, to enhance the generalization and small target feature extraction capability of the network. Then, the dilated convolution is incorporated into the Inceptionv3 to form a multi-branch parallel dilated convolution module, which can expand the receptive field of the feature layer and enhance the extraction of the target information. Finally, a dual-channel attention module is added, which adds the maximum pooling operation based on the efficient channel attention for deep convolutional neural networks (ECA) channel attention mechanism to highlight defect features and suppress background noise features. The experiments show that the accuracy of the system is improved while maintaining the faster detection speed. Among them, the LW-SSD algorithm has an accuracy improvement of 10.03% on the self-made dataset, a reduction of 58% in the number of model parameters compared to the Single Shot Multi-Box Detector (SSD) algorithm, and the detection speed reaches 48 frames per second.