Mobile-YOLO: An accurate and efficient three-stage cascaded network for online fiberglass fabric defect detection

The tolerance for different defects in the production process of fiberglass fabric varies. For acceptable defects, only relevant information needs to be recorded and printed, while for intolerable defects, the loom needs to be stopped promptly for handling. To address the issue of inefficient production of fiberglass fabric due to this situation, this paper proposes a task-modularized cascade network model for quality inspection of fiberglass fabric. The three stages cascaded detection network is composed of the improved MobilenetV3-small and the enhanced You Only Look Once v8 Nano (YOLOv8n). For MobilenetV3_small, the Squeeze- and-Excitation(SE) module in the Block layer with a 5 x 5 convolutional kernel is replaced with the Efficient Channel Attention (ECA) mechanism, enhancing channel information while making the model more lightweight. For YOLOv8n, a Global Attention Module (GAM) is added to the network, enhancing the ability to capture global information of input data, making the model more perceptual. Additionally, a Similarity Attention Module (SimAM) is added between the neck and head networks, enabling the network to better capture relevant information in the images, improving the quality of feature maps and enhancing network performance. Finally, a large number of defect images of fiberglass fabric were collected from an industrial site, and different datasets for the three stages were created. Numerous experiments were conducted, and when using all three stages for detection, the accuracy rate reaches 99.4%, with a parameter count of 16.8M and Frames Per Second (FPS) of 186. The cascade network has been successfully applied in the industrial field of fiberglass fabric and meets industrial production requirements.