Deep learning advancements in pothole detection: A comprehensive research and future directions

Road safety alongside maintenance planning heavily depends on accurate detections of potholes together with volume estimation. The current methods used to determine pothole depth and volume rates as time-consuming while also produce unreliable results. This research develops a Raspberry Pi automatic system using the HC-SR04 ultrasonic sensor combined with Pi Camera technology for enhancing immediate pothole surveillance and quantitative assessments. The system obtains imaging data from potholes; at the same time, it uses ultrasonic sensor depth readings to calculate volume sizes.

The sensor limitations during initial trials caused inconsistent depth readings, which subsequently affected the calculated volume measurements. Multiple trials of calibration, along with real-time multiple checks, produced system readings with a minimum accuracy of 0.68% that improved by an average of 2.05% between each test. Validation tests with GPS data showed the system-maintained reliability through measurements that varied between 3 to 5% of the actual values for practical deployment. Real-time data collection coupled with sensor-based monitoring demonstrates how it creates an effective solution for pothole assessment, which also proves economical.

Upcoming research efforts will direct their attention to sensor calibration process optimization alongside depth measurement precision enhancements and volume calculation optimization. Future detection accuracy improvement and road condition adaptability can be achieved by integrating machine learning analysis methods. The developed system supports intelligent road maintenance through its semi-automated approach while maintaining accurate pothole assessment capabilities on a large scale.