# A Road Defect Detection System Using Smartphones
## 1 Introduction  
This paper addresses research on the classification of road defects caused by various factors, such as weather and heavy vehicle traffic. Increased rainfall during the summer months can lead to road surface erosion and pothole formation, while the repeated transit of large vehicles can cause deformation and cracking of the road. Such road defects pose a serious risk to road safety and can lead to severe accidents, necessitating prompt detection and proper maintenance [1]. Effective maintenance of road infrastructure plays a crucial role in traffic safety and socio-economic stability. Prompt detection and appropriate response to road defects are essential in several key aspects. Road defects can lead to difficulties in vehicle control and increase the risk of accidents. Driving on roads with defects can cause serious accidents for both drivers and pedestrians. Additionally, cracks or unevenness on the road can damage vehicle tires or suspension systems, leading to increased repair costs and overall vehicle performance degradation [2]. The current method of reporting road defects primarily involves getting out of the vehicle to take a picture and report the issue. This can be dangerous on highways or heavily trafficked roads, and it has the disadvantage of taking a long time for repairs. In Canada, it takes less than 3 months to repair potholes during the winter. However, in the summer, it can take more than 24 months [3]. According to the Ministry of Land, Infrastructure, and Transport of Korea, the longest time taken to address a complaint was up to 99 days, with the duration being 99 days in 2020, 96 days in 2021, and 94 days in 2022, taking more than three months for three consecutive years [4]. Therefore, research is needed on ways to easily and safely detect road defects and share accurate defect locations. Recent research in road defect detection is categorized into three main technologies: The first is a vision-based approach using cameras, which detects road defects through image processing and deep learning. The image processing techniques are as follows: [PERSON] et al. [5] introduce an efficient vision-based method for pothole detection using red, green, blue (RGB) color space image segmentation, highlighting the potential of color analysis in identifying road defects. [PERSON] et al. [6] present a solution to detect potholes usingdisparity transformation and road surface modeling. For detecting multiple potholes, [PERSON] et al. [7] propose a real-time image processing method based on watershed techniques, emphasizing the efficiency of real-time applications. Additionally, a pothole classification model that uses edge detection techniques, one of the image processing methods, for road defect classification is proposed [8]. There is research detecting potholes with thermal cameras, focusing on using deep neural networks to detect and locate depressions in thermal images, exploring further the application of advanced neural networks in thermal imaging [9]. [PERSON]. [10] presents an attention-based joint framework for road and pothole segmentation, showcasing the application of attention mechanisms in road image analysis. It also comprehensively highlights the use of Convolutional Neural Network (CNN) for pothole detection and analysis, showing various innovative approaches to road surface monitoring for safety enhancement [11; 12; 13; 14]. These studies demonstrate the significant potential of CNN for enhancing road safety and maintenance through advanced image processing and analysis techniques. Each research project provides unique insights into the application of CNN, from thermal imaging to regional location-aware approaches, improving the detection and analysis of road potholes. The second technique is a 3D reconstruction method that generates a three-dimensional image of the road surface, allowing for precise identification of defects such as type, size, and depth. [PERSON] et al.