## 2 Background  
In construction projects, due to varied conditions such as temperature, humidity, geological environment, groundwater, external force, and technical problems, cracks in concrete structures have become a common issue in these projects that affects the normal operation of engineering facilities [1]. The efficient and accurate detection of cracks in concrete structures is a challenging key problem. The earliest method of crack recognition was manual patrol, where inspectors moved through the facility, detected crack positions with the naked eye [2], marked and took photos after discovery, and prepared for maintenance. However, the method of manual inspection highly depends on the professional ability of inspectors, and is affected by subjective factors. When the project site conditions are poor, it increases the difficulty of this work. Therefore, in recent years, crack detection has gradually shifted to a human-machine collaboration approach. This method involves using instruments on-site to collect acoustic or image information, which is then further analyzed and assessed. Compared with acoustic recognition, image recognition has fewer controllable variables and is more suitable for fast-moving recognition equipment, as it is not affected by the Doppler effect. This means that image information can more efficiently and accurately reflect the distribution of concrete cracks. However, in practical engineering, the time requirements for image acquisition, analysis, and judgment are more stringent, and a large range of cracks need to be detected with high precision in a short time. If using deep learning methods such as U-Net, there are higher hardware requirements such as GPUs. At the same time, the timeliness of calculation is insufficient. In other words, while there are still challenges in image-based concrete crack detection, the high precision, accuracy, and ease of implementation of image recognition make exploring effective methods for detecting cracks in concrete structures a pressing need in the field of structural detection. Currently, some researchers have conducted studies on crack detection in images using single algorithms. These algorithms are mainly divided into two categories: region growing algorithms and machine learning algorithms. [PERSON] conducted a comprehensive review of various methods for detecting pavement defects, particularly focusing on crack detection [3]. The review reported that the region growing method is particularly useful for outlining the internal characteristics of cracks, which edge detection algorithms may not fully capture. The selection of seed pixels is crucial in this method, as it significantly impacts the accuracy of image segmentation. The review also emphasized that machine learning techniques can analyze deeper features of the data compared to traditional image processing methods, which often only capture superficial characteristics. The authors mentioned that deep learning, in particular, has greatly improved the accuracy of pavement crack detection. The region growing method is an image segmentation technique that expands the region in all directions from a point that meets the detection criteria through pixel aggregation [4]. This method is based on similarity measurement, adding boundary pixels to the current region to ensure that their gray level is the highest within the region, thereby guiding the region to grow towards higher gray pixel levels. While researching concrete crack detection using the region growing algorithm, [PERSON] proposed the application of the region growth model in image recognition [5]. According to the characteristics of the complex point cloud model, a complex point cloud model segmentation method based on multi view region growth was proposed. At the same time, [PERSON] proposed an algorithm using region growth in the field of crack detection [6]. Further, [PERSON] applied the regional growth algorithm in the field of building crack detection [7].