# A Road Defect Detection System Using Smartphones
## 1 Introduction
[15] introduced a stereo-based 3D reconstruction method using a hybrid dense matching approach to highlight the use of stereo vision to represent the depressed areas of the road in detailed 3D. Like stereo vision, they combine deep learning to propose an automated pixel-level pavement distress detection method, emphasizing the integration of advanced imaging technology and AI for precise pavement analysis [16]. [PERSON] et al. [17] propose a pothole detection method based on 3D point cloud segmentation, explaining the use of three-dimensional spatial data for accurate pothole identification. Additionally, they suggest advanced methods for crack detection on paved roads using 3D data [18; 19]. Finally, in road surface monitoring and maintenance, the role of smartphones, sensor technology, and computational methods has become increasingly important, offering cost-effective, scalable, and innovative solutions for detecting depressions and various kinds of road defects. [PERSON] et al. [20] propose to use smartphone sensors and onboard diagnostics (OBD) to detect signs of road pavement abnormalities. This approach combines Artificial Neural Network (ANN) technology with smartphone sensors to capture the interaction between moving vehicles and the road surface, using observed interaction patterns to detect depressions. This method employs four metrics for analysis and demonstrates an approximately 90% detection accuracy. Initial results, which include additional road defects in the analysis and distinguish potholes from other pavement defects, are promising. This study underscores the value of low-cost pothole detection using smartphone sensors. Furthermore, [PERSON] et al. 's research [21], another smartphone-based pothole approach, extends smartphone usage beyond road monitoring to general environmental sensing. It explores how smartphones can detect environmental anomalies and demonstrates the versatile application of mobile sensors in various aspects of environmental monitoring. In a study proposed by [PERSON] et al. [22], which incorporates the Global Positioning System (GPS) into smartphones, an automated pothole detection system is introduced. They collected road condition data in urban areas using vehicles and smartphones and tested various machine learning classifiers based on this data. The Random Forest method proved to be the most effective for pothole classification, achieving a precision of 88.5% and a recall of 75%. Additionally, the system's diversity and robustness were validated through datasets from various road types. Research is also underway to detect not only potholes but also manhole covers on roads. [PERSON] et al. [23] introduce a smartphone-based method for detecting and classifying road manhole covers, expanding the scope of pavement monitoring using smartphone sensors. All road defect detection technologies have their own advantages and limitations, as summarized in Table 1. The vision-based approaches use cameras to extract images,detecting road defects through image processing and deep learning. This approach is useful for identifying the types and quantities of road defects but is heavily affected by lighting, limiting its use during nighttime or rainy weather. The 3D reconstruction methods have the advantage of providing detailed insights into the types, sizes, and depths of road defects. However, this requires the use of detecting equipment, which incurs additional costs for road defects. The vibration sensor-based road defect detection techniques are less affected by environmental influences such as weather and time and are a cost-effective solution. However, these techniques have the disadvantage of requiring a lot of preliminary data collection to ensure high accuracy. This paper proposes a road defect detection system based on vibration sensors, specifically accelerometers. The cornerstone of our methodology is an automated system for data collection specifically designed to facilitate the classification of various road conditions essential for identifying road defects.