## 2 Related Work
[16] developed a real-time road surface detection model using a Mask Region-based Convolutional Neural Network (Mask R-CNN), trained and validated with images manually labeled using Microsoft Visual Object Tagging Tool (VoTT). This approach represents a significant leap forward in applying deep learning to road surface assessment, providing high accuracy and real-time processing capabilities. [PERSON] et al. [12] proposed pixel-wise road crack segmentation using U-Net, another kind of CNN model, and made a comparison with traditional machine learning algorithms, such as SVM and KNN. A more detailed comparison of vision-based methods can be found at [PERSON] et al. [11], along with some related BFRS-detecting datasets. However, since the collected image or video can be affected by the light, noise samplings, and obstructed sampling, especially in the environment with poor ambient light, or BFRS is blocked on the ground by turbid water and other objects, it becomes difficult to detect BFRS for these vision-based methods. In the sensor-based method, BFRS is captured using different kinds of sensors, such as LiDAR, accelerometer, gyroscope, GPS, and related auxiliary sensor [9; 19; 20; 21], with the assumption that abnormal changes caused by BFRS can be recorded during the driving period. One way to collect BFRS is by utilizing the LiDAR sensor, which scans the road surface and detects BFRS based on abnormal changes between the point cloud-fitted surface and typical road surface. Building on the high-accuracy methods described by [PERSON] et al. [18], which utilize a 3D point cloud from stereo cameras for detecting and reconstructing curbs and road surfaces, there is a shift in the approach with [PERSON] et al. [30]. It employed an ultrasonic sensor to monitor road surfaces, using the Dynamic Time Warping (DTW) technique for image processing to match and locate different kinds of BFRS via a GPS sensor. While both methods provide precise measurements of the shape and type of BFRS, they also entail significant costs due to the professional tools required. An alternative approach involves the use of specially designed IoT sensors to collect BFRS data. [PERSON] et al. [31] proposed a road surface monitor method using the on-board vehicular sensor system, which was composed of a regular PC computer, low-cost microphone sensor, and GPS receiver. Potholes were detected based on selected thresholds for the microphone sensor recordings. In addition, sensor recordings can be uploaded to the web server through IoT networks [2; 4], and BFRS is detected based on multi-source datasets. These methods detect BFRS based on the vibration of vehicles during the driving period, with an acceleration sensor, GPS sensor, and related auxiliary sensor. In recent years, smartphones equipped with multiple sensors have emerged as a popular tool for collecting road condition data. [PERSON] et al. [3] utilized this technology to develop a road roughness assessment method. By leveraging the built-in GPS receiver and accelerometer in smartphones, they gathered a spatial series of geo-referenced vertical accelerations of the road surface. They further enhanced this approach by creating a mobile crowdsensing system, which integrates data from multi-source smartphones and a web-based server. This system computes the International Roughness Index (IRI) for each road segment to assess surface quality. Building on this foundation, [PERSON] et al. [8] conducted experiments and developed a prototype system named MAARGHA, designed to estimate the condition and surface type of road surfaces. This work is part of a broader trend in which researchers such as [PERSON] et al. [6], [PERSON] et al. [32], [PERSON] et al. [33], and [PERSON] et al. [24] have also contributed, each exploring similar methodologies to evaluate and improve road surface assessment using advanced sensor technology and innovative data integration techniques. [PERSON] et al.