# CL-YOLOv8: Crack Detection Algorithm for Fair-Faced Walls Based on Deep Learning
###### Abstract  
Cracks pose a critical challenge in the preservation of historical buildings worldwide, particularly in fair-faced walls, where timely and accurate detection is essential to prevent further degradation. Traditional image processing methods have proven inadequate for effectively detecting building cracks. Despite global advancements in deep learning, crack detection under diverse environmental and lighting conditions remains a significant technical hurdle, as highlighted by recent international studies. To address this challenge, we propose an enhanced crack detection algorithm, CL-YOLOv8 (ConvNeXt V2-LSKA-YOLOv8). By integrating the well-established ConvNeXt V2 model as the backbone network into YOLOv8, the algorithm benefits from advanced feature extraction techniques, leading to a superior detection accuracy. This choice leverages ConvNeXt V2's recognized strengths, providing a robust foundation for improving the overall model performance. Additionally, by introducing the LSKA (Large Separable Kernel Attention) mechanism into the SPPF structure, the feature receptive field is enlarged and feature correlations are strengthened, further enhancing crack detection accuracy in diverse environments. This study also contributes to the field by significantly expanding the dataset for fair-faced wall crack detection, increasing its size sevenfold through data augmentation and the inclusion of additional data. Our experimental results demonstrate that CL-YOLOv8 outperforms mainstream algorithms such as Faster R-CNN, YOLOv5s, YOLOv7-tiny, SSD, and various YOLOv8n/s/m/1/x models. CL-YOLOv8 achieves an accuracy of 85.3%, a recall rate of 83.2%, and a mean average precision (mAP) of 83.7%. Compared to the YOLOv8n base model, CL-YOLOv8 shows improvements of 0.9%, 2.3%, and 3.9% in accuracy, recall rate, and mAP, respectively. These results underscore the effectiveness and superiority of CL-YOLOv8 in crack detection, positioning it as a valuable tool in the global effort to preserve architectural heritage. ConvNeXt V2; crack detection; fair-faced wall; LSKA; YOLOv8 2024 14 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9421 9422 9421 9422 meet the protection needs of large-scale historical buildings. While some simple image processing techniques offer convenience compared to manual inspections, they still suffer from issues such as missed detections and false alarms. With the advancement of technology, various techniques beyond artificial intelligence have also played significant roles in this field. For example, [PERSON], [PERSON] et al. [1], integrated intelligent monitoring systems with digital image processing technology for building monitoring and maintenance. By using high-resolution cameras and image processing algorithms, cracks can be detected early and their progression can be monitored. Digital image processing techniques, such as filtering, edge detection, and threshold segmentation, are employed to extract the geometric features of cracks. While these methods perform well in relatively uniform environments, their robustness under complex lighting and environmental conditions is not as strong as that of deep learning techniques. Nonetheless, these traditional methods remain fundamental in building monitoring, especially for simpler crack detection tasks. [PERSON] et al. [2]. discussed the application of material modification in building structures. By using fly ash (FA) and nano-silica (NS), the crack resistance of materials can be significantly improved. This approach enhances the mechanical properties of materials, reducing the formation of cracks and addressing potential issues in the design and construction stages of building structures. This type of material science technology serves as a preventive measure for cracks and can complement modern detection techniques.