# McAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects
## 2 Related Work  
### Defect Detection  
Defect-detection algorithms can be categorized into single-stage and two-stage detection algorithms, depending on whether the model generates candidate bounding boxes.  
The mainstream method for two-stage detection algorithms is the Region-CNN series [3; 4; 5]. For example, [PERSON] et al. [6] presented an improved faster R-CNN model for detecting in-shell walnuts with shirveled and empty-shell defects. They incorporated a feature pyramid network and utilized the region-of-interest alignment and softer NMS modules to enhance the model's detection precision. [PERSON] et al. [7] introduced the multi-stage balanced R-CNN defect-detection method, incorporating deformable convolutions and balanced feature pyram to enhance feature-extraction capabilities. Furthermore, they applied balanced L1 loss and IoU balanced sampling to improve the model's detection accuracy. [PERSON] et al. [8] proposed the IA-Mask R-CNN detection method for surface-defect detection on automotive engine parts. They determined the optimal anchor scale through labeled data analysis. Although two-stage detection algorithms can reduce reliance on manual intervention, these algorithms require separate training for candidate box generation and object detection, which consumes more training time and resources. Additionally, these algorithms are slower, making applying them to real-time scenarios challenging.  
The mainstream method for single-stage detection algorithms is the You Only Look Once (YOLO) series [9; 10; 11; 12; 13]. These algorithms directly predict the defects category and regress location information, usually boasting faster detection speed. For example, [PERSON] et al. [14] proposed an improved YOLOv5 model for surface-defect detection in aerospace engine components. This model utilizes the K-means clustering algorithm to optimize the size of anchor points and incorporates the efficient channel mechanism in the backbone network to enhance feature representation. [PERSON] et al. [15] introduced the DME-YOLO model for defect detection in the appearance of high-frequency transformers. By reusing features, they accelerated the detection speed. Additionally, they designed the multi-information-source spatial attention module to enhance feature-extraction capabilities and employed the EIoU loss function to improve bounding box regression accuracy. [PERSON] et al. [16] proposed an insulator defect-detection algorithm that improves upon YOLOv7, utilizing the K-means++ clustering algorithm to generate defect bounding boxes more suitable for model detection. Subsequently, they incorporated coordinate attention and HorBlock modules to bolster the network's expressive capacity. Lastly, they introduced SloU and focal loss functions to expedite model convergence. [PERSON] et al. [17] presented the ODCA-YOLO algorithm for timber-defect detection, integrating coordinate attention and omni-dimensional dynamic convolution to amplify the model's feature-extraction prowess. Moreover, they applied the shuffle concept within the HorBlock module to heighten the model's recognition accuracy. Although the studies mentioned above apply the YOLO series algorithms to defect detection with good generalization and detection performance, the improvements usually increase the depth and complexity of the network, resulting in an increased number of parameters, which is unsuitable for deployment on edge devices.