Automatic road disaster detection technologies: a comprehensive review of developmental progress

As a strip building laid on the ground, highways are prone to road hazards such as pavement cracks, potholes, subsidence, tension cracks, extrusion bumps, and distortion deformation with the increase of service life. If these hazards are not detected and handled in a timely manner, they will cause significant traffic safety hazards. Therefore, regular road hazard detection is particularly important. Traditional road hazard detection methods are manual detection or detection with road detection vehicles, but due to the limitations of detection methods and processing technologies, they cannot meet the real-time requirements for road surface detection. Automatic road surface defect detection methods have proved their effectiveness in traditional road surface detection, and their real-time, fast, and accurate detection capabilities have established their potential advantages in automatic detection methods for road surface defects. This paper comprehensively analyzes the automatic detection and processing technology of road surface defects, explores different dimensions of automatic detection and processing technology for road surface defects, conducts detailed comparative analysis of road surface defect detection technology based on deep learning, Overall, the above work can promote the development of road surface defect technology and provide reference and basis for future road hazard detection methods.