## 1 Introduction  
Highway transportation constitutes the backbone of human social infrastructure, with highway bridges, as critical components, playing a vital role in the entire system. Their safety directly impacts the security of vehicles and passengers. However, during their service life, various bridge problems gradually emerge, causing damage to the infrastructure and reducing its usability. In particular, cracks and spatial morphological changes hinder the bridge from functioning optimally or operating under normal conditions. Under these circumstances, non-contact bridge inspection methods, such as point cloud data collection, are more convenient for detecting visible bridge issues. These methods can simultaneously collect information on both the overall bridge structure and its individual components, ensuring the completion of inspection tasks from both macro and micro perspectives. Thus, the application of point cloud data in bridge structure inspection presents considerable advantages. The key to accurately representing structures lies in their point cloud characteristics, which typically include essential geometric and topological information about the entity's surface. Feature extraction refers to the process of analyzing target point cloud data to capture points that reflect the geometric and textural features of the entity, followed by constructing feature lines and surfaces based on these key points. One important task in this study is to identify these morphological feature points, as they can quickly and effectively reveal core information about the structure. In terms of crack detection, prior research has focused on various methods and techniques. [PERSON] et al. (2018) explored the use of commercial drones equipped with high-resolution sensors to identify cracks in aging concrete bridges, proposing drone-based inspection as a promising strategy [1]. [PERSON] (2020) optimized a design for identifying large-volume concrete cracks using image processing techniques, achieving higher recognition accuracy compared to traditional methods [2]. [PERSON] et al. (2021) designed a method that combines convolutional neural networks (CNNs) and Fully Convolutional Networks (FCNs) for crack detection and bridge condition monitoring, addressing the challenge of identifying non-crack areas containing interference [3]. CNNs represent a type of deep learning model primarily employed for image recognition and processing. Their distinctive convolutional layers enable CNNs to effectively identify intricate patterns and features within images. In the context of bridge crack detection, CNNs can be trained to recognize specific patterns associated with cracks, thereby facilitating automated crack detection. FCNs are a variant of CNNs that consist entirely of convolutional layers and are particularly adept at pixel-level image analysis tasks. FCNs are especially useful in image segmentation tasks, as they can generate probability maps of the same size as the input image, indicating the likelihood of each pixel belonging to a particular category. In bridge crack detection, FCNs can be utilized to accurately segment images, differentiating between crack and non-crack regions, even in the presence of interference. The study demonstrates that the combination of CNNs and FCNs can effectively detect bridge cracks, overcoming the challenge of identifying cracks amidst non-crack areas with interference. This combined approach enhances the accuracy and reliability of bridge crack detection. [PERSON] et al. proposed an automatic bridge surface crack detection technique based on 2D-APES and mobile machine vision, highlighting the efficiency, ease of operation, and cost-effectiveness of computer vision in crack detection [4]. [PERSON] et al. (2022) introduced a multi-stage semi-supervised active learning framework for crack detection, segmentation, and measurement, including pretraining, retraining, segmentation, and width measurement [5]. [PERSON] et al.