## 2 Related Work  
As an important factor for transportation safety and driving experience, BFRS detection has long been the heated point for years [3; 6; 11; 16]. Hence, based on the sampling device, related research can be categorized into intuitive vision-based methods and sensor-based methods. In the intuitive vision-based method, BFRS is collected directly by the camera through images or videos [10; 11; 13; 26]. Images or videos can be collected by specially designed cameras or smartphones during the movement on the road surface. Then, collected images or videos are resampled as a single image with a selected time interval, and then gradients, edges, Haar feature, and some other similar features are computed for BFRS. These image-based techniques are well demonstrated by several recent studies, each employing different methodologies to detect and analyze road surface conditions. [PERSON] et al. [27] focused on detecting cracks in road surface images using a Back Propagation Neural Network (BPNN). After successfully identifying the cracks, they utilized image processing techniques to extract the related geometric shapes of each crack, demonstrating the practical application of neural networks in road maintenance. Following a somewhat similar approach but with different tools, [PERSON] et al. [28] applied histograms of oriented gradients (HOG) to capture textural features of the road surface. They fed these HOG features into a Naive Bayes classifier to detect potholes. Once identified, the potholes' geometric information was extracted using the graph-cut image segmentation method, highlighting the integration of machine learning and advanced image processing techniques. Expanding on these methods, [PERSON] et al. [10] computed the Haar feature of each sampled image and trained a classifier using the Adaboost algorithm to detect cracks on the road surface. This approach emphasizes the robustness of feature-based machine learning algorithms in identifying specific types of road damage. [PERSON] et al. [29] collected RGB images of road surfaces along with the GPS sensor to detect the accurate geometric information of potholes. The RGB image is first enhanced as the gray image, transformed and filtered using the 2D discrete wavelet transformation algorithm, and BFRS are clustered using the fuzzy-c means method. Finally, BFRS are morphologically reconstructed and compared with ground truth objects for validation. In addition, based on photogrammetry or computer vision theory, a mono camera can also detect the generated depth image using the binocular stereo camera. There are various innovative approaches using both image processing and machine learning techniques. [PERSON] et al. [14] developed a hybrid method that fuses monocular images with LiDAR point clouds to detect road surfaces in diverse scenarios. They utilized classifiers for both images and point clouds based on boosted decision trees and integrated the classification results using Conditional Random Fields (CRFs). Building on the use of imaging for detection, [PERSON] et al. [13] constructed a pothole detection system based on stereovision. By calculating distances from stereo camera-collected image pairs and utilizing auxiliary GPS data, they were able to precisely determine the size, volume, and position of each pothole, demonstrating the effectiveness of integrating stereo vision with geospatial data. Further exploring stereo imaging, [PERSON] et al. [15] detected potholes using stereo images mounted on a vehicle. They employed random sample consensus (RANSAC) to mitigate the effects of potential outliers during the 3D road surface reconstruction, transformed dense disparity maps to enhance the distinction between damaged and undamaged road areas, and used threshold selection for BFRS detection. These methods underscore the growing influence of deep learning technologies in improving the accuracy of detection systems. Continuing this trend, [PERSON] et al.