An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data

• Main Authors: Chao Wu, Zhen Wang, Simon Hu, Julien Lepine, Xiaoxiang Na, Daniel Ainalis, Marc Stettler
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Sensors
• Subjects: road quality monitoring; shock detection; pothole detection; crowdsourced data; support vector machine; random forest
• Online Access: https://www.mdpi.com/1424-8220/20/19/5564

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.