# McAW-YOLO: An Efficient Detection Model for Ceramic Tile Surface Defects
## 1 Introduction  
Ceramic tiles are commonly used for floor and wall decoration in construction. However, ceramic tiles can fall victim to various defects during manufacturing [1]. These defects could arise from various sources, including the raw materials used, the production processes' intricacies, and the manufacturing equipment's reliability. Among the commonly observed defects are: physical damages, such as chips or breaks; structural flaws, like cracks or deformations; and cosmetic issues, including discolorations or variances in hue and pattern. The presence of these defects not only diminishes the aesthetic value of the tiles but could also compromise their structural integrity, potentially shortening their functional lifespan and posing safety risks. Moreover, tiles that consistently demonstrate a high defect rate could tarnish a manufacturer's reputation, eroding consumers' trust. Such tiles invariably fail to meet industry standards, reducing market competitiveness. Consequently, the sales and profitability of these subpar products takes a hit, jeopardizing the company's brand image and long-term viability. The detection of defects in ceramic tiles has predominantly depended on manual visual detection by skilled workers [2]. In this traditional method, inspectors meticulously examine each tile for defects, ranging from minute cracks to discolorations. However, the manual process, despite the expertise of the workers, came with inherent challenges. Subjectivity, fatigue, and the propensity for human error rendered it inefficient, especially for the demands of large-scale, modern-day production lines. In light of these challenges, computer vision offered promising solutions. The introduction of detection algorithms, particularly those harnessing the power of deep learning, has revolutionized tile-defect detection. These algorithms, known for their high precision, excel at classifying the nature of defects and pinpointing their exact locations on the tiles. The resultant reduction in errors, previously arising from manual detections, led to a noticeable enhancement in production efficiency. Moreover, the adoption of computer vision transcended mere defect detection. It offered automation in image and video analysis, significantly reducing the need for manual oversight and, in turn, easing the burden on human inspectors. The multifaceted benefits of integrating this technology were hard to ignore. Beyond the obvious savings in labor costs, there was a strategic advantage: companies embracing this innovation stood out in the competitive market, reinforcing their brand reputation and ensuring continued consumer trust in the quality of their products. This paper proposed a lightweight ceramic-tile defect-detection algorithm called MCAW-YOLO. This algorithm aimed to improve the model's detection accuracy while reducing the number of parameters. The effectiveness of the improved algorithm was verified through ablation experiments. Moreover, comparisons were made with other YOLO-series algorithms, underscoring the superior performance of MCAW-YOLO. The main research methods are as follows:  
1. Introducing the mobile-friendly vision transformer (MobileVitTV3) into the backbone network to capture defect information. By combining the local information provided by convolution and the global information provided by the transformer, the model could better capture features at different scales and abstraction levels, thereby improving model performance. 2. Designing the lightweight bidirectional feature pyramid network (Light-BiFPN) feature fusion network in the neck network to obtain feature maps at different scales through cross-layer connections. This promoted the interaction between shallow localization and deep semantic information, enhanced the fusion of multi-scale features, and effectively reduced the number of model parameters. 3.