## 2 Background
This practice shows that when the crack width is greater than 0.15 mm and there is no water stain and non-destructive interference, the detection accuracy can reach 90%. Another approach to using regional growth in crack detection starts with Region of Belief (ROB) seeds, determining the search scope based on specially devised rules, and merging ROBs with different regions following a similarity criterion that considers various cues [8]. The process includes extracting the orientation and length of each ROB, starting from the ROB with the largest length as the seed, generating a set of growing regions based on matching criteria, merging ROBs with regions in the set based on spatial relationships, and repeating the process until all ROBs are grown. Noise regions not involved in growth are removed. Overall, the region growing algorithm appears to be highly accurate and efficient in the field of concrete crack detection. However, the aforementioned research results do not consider the issue of recognition accuracy under interference conditions. When multiple factors interfere on-site, the accuracy of the region growing algorithm often significantly decreases because it cannot handle disturbances such as dust and water stains. Compared with machine learning methods, the region growth algorithm can solve the problem faster, but will cause serious over-segmentation and under-segmentation. To address the interference imposed by construction site environments on recognition algorithms, numerous scholars have resorted to using machine learning algorithms for the detection of concrete cracks. [PERSON] compared various CNN methods in their review, specifically highlighting different architectures and their performance in crack detection [9]. They mainly compared the architectures of CNN, datasets, training methods, loss functions, performance metrics, and computational efficiency. Among these methods, R-CNN and U-NET were found to be more suitable for crack detection. U-Net has the advantage of data efficiency and R-CNN is good at distinguishing different types of cracks. Therefore, we have mainly surveyed these two kinds of machine learning methods. [PERSON] introduced an enhanced Cascade R-CNN for recognizing tunnel targets, which sharpens crack details, facilitating the detection of smaller cracks and the classification of tunnel defects [10]. However, this method necessitates the configuration of intricate hardware parameters, such as focusing and sub-frequency conversion modules. [PERSON] proposed a crack recognition approach that integrates digital image processing and structured light recognition [2]. This involves capturing numerous images of the target object, extracting pixel position information, converting it to real-world spatial dimensions, and then performing a series of image processing tasks to achieve target recognition and positioning. [PERSON] and his team utilized U-Net, a convolutional neural network architecture introduced by [PERSON] in 2015 for swift and precise image segmentation [9; 10]. Its speed and support for multi-channel feature maps make it suitable for rapid crack detection. This team also designed a crack detection dataset for training and testing, which comprises 10,100 images, resulting in a training duration of nearly 10 h and a test time of approximately 3 s per image. In this study, the U-Net method served as a control, highlighting the substantial improvement in concrete crack detection accuracy achieved through machine learning algorithms. Another improved method for detecting cracks involves the Swin Transformer module, which enhances the network's ability to focus on and express crack features, thereby improving detection accuracy [11]. However, implementing these algorithms is often very complex, difficult, and costly. In practical engineering, there is a need to balance high efficiency, high accuracy, and low cost. Therefore, the adoption of machine learning-based concrete crack detection methods faces considerable resistance in practical engineering applications.