RC-YOLOv5s: for tile surface defect detection

To solve the problems of complex surface texture of magnetic tile, cumbersome process of traditional detection algorithm and low detection accuracy, this paper proposes a deep learning-based detection model: RC-YOLOv5s. The model incorporates two new structures: Res-Head and Drop-CA, where Res-Head enhances the feature fusion and information exchange between different layer structures and Drop-CA alleviates the case that the model pays too much attention to the defect target and reduces the missed detection rate of the model. Compared with YOLOv5s, the detection accuracy of the proposed model is improved by 1.83%, the missing rate is reduced from 1.673 to 0.372%, and the average detection frame rate of a single image reaches 41.67, which meets the requirements of real-time and accuracy of tile surface detection.