Detection of Potholes Using Image Processing Method

Potholes are a common problem on roads, caused by weather, vehicle activity, and poor maintenance. Potholes can be hazardous for drivers, cars, and motorcycle riders. Potholes are often filled with asphalt or concrete. A methodology for automatically identifying potholes on road surfaces using computer vision methods is potholes detection utilizing image processing. This technique can be used to improve road maintenance by quickly locating potholes, enabling early repairs, and lowering the risk to drivers and their cars. This study emphasizes a Gaussian noise filtering technique for the developed infrastructure of image pre-processing stage. Thus, this study also suggests four methods for segmentation detecting potholes in images: image thresholding (Otsu), Canny edge detection, K-means clustering, and fuzzy C-means clustering. The effectiveness of the different image segmentation techniques was tested in MATLAB 2019a, and the results were generated in terms of accuracy and precision. The results were compared with each other to draw a conclusion on their viability.