A Critical Appraisal of Various Implementation Approaches for Real-time Pothole Anomaly Detection: Toward Safer Roads in Developing Nations

Abstract

Road infrastructure is essential to national security and growth. Potholes on the road surface cause accidents and costly automotive damage. Novel technology that detects potholes and alerts drivers in real-time may address this challenge. These approaches can improve road safety and lower vehicle maintenance cost in resource-constrained developing nations. This study reviews deep learning and sensor-based pothole detection approaches. An analysis shows that deep learning computer-vision-based algorithms are the most accurate, but computational and economic constraints limit their use in developing nations like Nigeria. Meanwhile, the sensor-based solutions are cost-effective and can be utilized in developing nations for pothole detection.

Keywords

• deep learning
• detection
• computer vision
• LiDAR
• potholes
• road