Textile defect detection based on multi-proportion spatial attention mechanism and channel memory feature fusion network

This paper presents a textile defect detection method that utilizes a multi-proportion spatial attention mechanism and channel memory feature fusion network by addressing the difficulties presented by complicated shapes and large size variations. In particular, a multi-proportion spatial attention mechanism (MPAM) is introduced, which employs multi-proportion convolution to improve the backbone network's capacity to detect non-uniform structural defects. Additionally, the generality and adaptability of the model are enhanced by a multi-scale spatial pyramid pooling structure (MS-SPP). Second, a channel attention mechanism-based memory feature fusion network is developed, which incorporates channel attention to adaptive weight the feature channels, focusing on crucial information channels to efficiently fuse contextual features and enhance the model's memory capacity. Finally, a novel efficient Wise-IoU (EWIoU) loss function is proposed, which utilizes a dynamic non-monotonic focusing mechanism to increase the penalty on distance measurement, thus enhancing the model's detection performance. Experiment findings on the ZJU-Leaper and Tianchi textile datasets reveal that compared to the YOLOv7 baseline, the method in this paper has an increase of 6.5 and 2 percentage points, respectively, and the detection accuracy is better than most existing networks.