Convolution neural networks for pothole detection of critical road infrastructure. (April 2022) - Pandey, Anup Kumar; Iqbal, Rahat; Maniak, Tomasz; Karyotis, Charalampos; Akuma, Stephen; Palade, Vasile
Abstract: A well developed and maintained highway infrastructure is essential for the economic and social prosperity of modern societies. Highway maintenance poses significant challenges pertaining to the ever-increasing ongoing traffic, insufficient budget allocations and lack of resources. Road potholes detection and timely repair is a major contributing factor to sustaining a safe and resilient critical road infrastructure. Current pothole detection methods require laborious manual inspection of roads and lack in terms of accuracy and inference speed. This paper proposes a novel application of Convolutional Neural Networks on accelerometer data for pothole detection. Data is collected using an iOS smartphone installed on the dashboard of a car, running a dedicated application. The experimental results show that the proposed CNN approach has a significant advantage over the existing solutions, with respect to accuracy and computational complexity in pothole detection.