Surface Defect Detection of Mosaic Ceramic Walls Based on Deep Learning

The surface defects of mosaic ceramics on building walls not only compromise aesthetics but also pose structural safety and high-altitude falling hazards. Therefore, accurate defect detection is essential. Leveraging drone image data, this paper proposes a mosaic ceramic wall surface defect detection method based on an improved YOLO11 technique. Specifically, the standard convolution module of YOLO11 is replaced by the morphological-dilated-attention coupled convolution proposed in this study. Additionally, the large separable kernel attention module is fused with the channel and position self-attention module, and the spatial grouping enhancement technique is introduced into the backbone network. Together, these modifications establish the YOLO11-MLS model, which achieves both higher accuracy and model lightweighting. Experimental results demonstrate a detection accuracy of 92.9%, representing a 2.3% improvement over the original YOLO11. Moreover, the model achieves a single-image detection time of 22.8 ms, while reducing parameters and computation by 23.3% and 20.6%, respectively, realizing efficient, accurate, and lightweight detection of mosaic ceramic wall surface defects.