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.

Machine learning (ML) classifiers have lately gained traction in the realm of intelligent transportation systems as a means of enhancing road navigation while also assisting and increasing automotive user safety and comfort. The feature extraction stage, which defines the performance accuracy of the ML classifier, is critical to the success of any ML classifiers used. Nonetheless, the efficacy of various ML feature extractor filters on image data of road surface conditions obtained in a variety of illumination settings is uncertain. Thus, an examination of eight different feature extractor filters, namely Auto colour, Binary filter, Edge Detection, Fuzzy Color Texture Histogram Filter (FCTH), J-PEG Color, Gabor filter, Pyramid of Gradients (PHOG), and Simple Color, for extracting pothole anomalies feature from road surface conditions image data acquired under three environmental scenarios, namely bright, hazy, and dim conditions, prior classification using J48, JRip, and Random Forest ML models. According to the results of the experiments, the auto colour image filter is better suitable for extracting features for categorizing road surface conditions image data in bright light circumstances, with an average classification accuracy of roughly 96%. However, with a classification accuracy of around 74%, the edge detection filter is best suited for extracting features for the classification of road surface conditions image data captured in hazy light circumstances. The autocolor filter, on the