# CL-YOLOv8: Crack Detection Algorithm for Fair-Faced Walls Based on Deep Learning
###### Abstract
In addition to image-based detection methods, modern crack detection also includes ultrasonic testing, infrared thermography, and laser scanning technology. These non-AI methods each offer advantages in crack detection for building structures. For instance, digital image processing techniques remain a reliable foundational approach, while material enhancement technologies help to reduce the need for subsequent crack detection by preventing their occurrence. By combining these methods with artificial intelligence, more comprehensive and precise monitoring and crack management strategies can be formulated. In recent years, with the rapid development of artificial intelligence and deep learning, significant progress has been achieved in wall defect detection using convolutional neural network (CNN)-based object detection algorithms. Based on the different processes of candidate region generation and recognition during detection, object detection algorithms can be classified into the two following main types: two-stage detection algorithms and one-stage detection algorithms. Among two-stage detection algorithms, common examples include the R-CNN series (R-CNN, Fast R-CNN, and Faster R-CNN) and Mask R-CNN [3], which achieve object detection by first generating candidate regions and then recognizing them. [PERSON] et al. proposed a detection method for cracks in masonry structures using the architecture of the Faster R-CNN object detector and the Mobilenetv2 network [4]. They introduced a new approach called \"progressive detection\" to enhance the robustness of detection. [PERSON] et al. proposed a method for detecting cracks in building facades using unmanned aerial vehicles (UAVs) and computer vision technology. They selected Mask R-CNN, which demonstrated the best performance, as the detection network, achieving an average precision of 0.9 [5]. Although two-stage detection algorithms demonstrate an exceptional accuracy in detecting targets in complex scenes, they need to first generate candidate regions, extract features, and then recognize each candidate region, leading to relatively slow detection speeds. This limitation makes it challenging to respond to real-time requirements, and the generation of candidate regions and subsequent feature extraction involve a certain redundancy and repetitive computations, thereby reducing algorithm efficiency. One-stage algorithms eliminate the step of candidate region generation and directly perform feature extraction and object detection on the entire image, significantly simplifying the detection process and improving the detection speed. The YOLO (You Only Look Once) series algorithms represent one-stage detection algorithms, including YOLOv1, YOLOv2, YOLOv3, YOLOv4, and YOLOv5. The YOLO algorithm divides an image into grids, with each grid being relied on to predict targets within its region. By simultaneously performing feature extraction, target classification, and position regression, the YOLO algorithm achieves real-time object detection, dramatically enhancing detection speed while maintaining detection accuracy. Additionally, the SSD (Single-Shot MultiBox Detector) series algorithms and OverFeat are also designed based on the one-stage concept, continuing to improve the performance of one-stage algorithms through different network structures and optimization strategies. [PERSON] et al. deployed an automated strategy based on the YOLOv5 object detection model for crack detection in brick structures, enabling the precise localization of cracks through bounding box capture. The model achieved real-time health monitoring capabilities [6]. [PERSON] et al. designed a YOLO algorithm, named MOD-YOLO [7], which preserves the original contour. By introducing depth-wise separable convolution and Discriminative and Average Fusion Coordinated Attention (DAF-CA), they improved the YOLOX algorithm and applied it to crack detection in civil infrastructure. [PERSON] et al.