Tile Defect Recognition Network Based on Amplified Attention Mechanism and Feature Fusion

For the current situation of low AP of tile defect detection with incomplete detection of defect types, this paper proposes YOLO-SA, a detection neural network based on the enhanced attention mechanism and feature fusion. We propose an enhanced attention mechanism named amplified attention mechanism to reduce the information attenuation of the defect information in the neural network and improve the AP of the neural network. Then, we use the EIoU loss function, the four-layer feature fusion, and let the backbone network directly involved in the detection and other methods to construct an excellent tile defect detection and recognition model Yolo-SA. In the experiments, this neural network achieves better experimental results with an improvement of 8.15 percentage points over Yolov5s and 8.93 percentage points over Yolov8n. The model proposed in this paper has high application value in the direction of tile defect recognition.