AYOLOv7-tiny: Towards efficient defect detection in solid color circular weft fabric

Abstract

This article presents a solid color circular weft fabric defect detection method based on AYOLOv7-tiny. The aim of the development of this network is to enable real-time defect detection in the production of large circular machine fabrics. The network has a good accuracy rate, fast detection speed, and a lightweight model. In the YOLOv7-tiny network, a space-to-depth layer followed by a non-strided convolution layer is introduced to enhance the feature extraction capability, improve image sharpness, address issues such as uneven grayscale and difficult detection of minor defects, and simplify the model complexity while reducing computation. Additionally, we combined the Squeeze and Excitation (SE) and Spatial Attention Module (SAM) based on Convolutional Block Attention Module (CBAM) to construct the Hybrid Attention Module (SC), which is integrated into the YOLOv7-tiny network. The SC module increases the weight of important features, enhances the feature extraction capability, improves image segmentation, and enhances the accuracy of the network's location information. Through extensive experiments with a dataset of large circular machine solid color circular weft fabric defect collected from an industrial site, the results show that AYOLOv7-tiny has a detection accuracy (mean average precision) of 98.7%, a detection speed of up to 333 frames per second, and a computational complexity of only 4.4 GFlops, which is better than the current mainstream surface defect detection models. The detection accuracy, detection speed, and model complexity of the AYOLOv7-tiny network all meet the real-time detection requirements of the industry and have been successfully used for real-time detection of large circular machine fabric defects.