Automated potholes detection using vibration and vision-based techniques

Roads serve as vital parts of our infrastructure, providing as crucial conduits for people's mobility and connectivity. However, the growing number of vehicles on the road has resulted in an increase in pavement strain and degradation, which has a substantial impact on the entire riding experience. To achieve a high-quality surface, roadways must be consistently monitored and maintained.
In recent years, transportation infrastructure agencies and governments have shown a rising interest in leveraging new technologies to monitor road pavements. This interest derives from the difficult and time-consuming nature of manual and instrumented techniques. Automated technologies have arisen as a response to these issues, notably in recognizing pavement deterioration, such as the common problem of potholes.
The objective of this research is to identify potholes using two low-cost automated techniques: a vibration-based method that uses the G-Sensor Logger application and a vision-based way that uses image processing. On the same roads, both approaches were employed and compared, with manual surveying utilized to validate the results. The results showed that vision-based strategies were more effective than vibration-based methods.
Finally, although vibration-based analysis is appropriate for routine monitoring, vision-based analysis provides a more comprehensive and in-depth examination of road conditions. These discoveries will help future efforts to better monitor and maintain road surfaces, ensuring a smooth and safe travel experience for everybody.