Convolutional neural networks based potholes detection using thermal imaging

The presence of potholes on the roads is one of the major causes of road accidents as well as wear and tear of vehicles. In order to solve this problem, various techniques have been implemented ranging from manual reporting to authorities to the use of vibration-based sensors to 3D reconstruction using laser imaging. But all these techniques have some drawbacks such as the high setup cost, risk while detection or no provision for night vision. Therefore, the objective of this work is to analyze the feasibility and accuracy of thermal imaging in the field of pothole detection. After collecting a suitable amount of data containing the images of potholes under various conditions and weather, and implementing augmentation techniques on the data, convolutional neural networks approach of deep learning has been adopted, that is a new approach in this problem domain using thermal imaging. Also, a comparison between the self-built convolutional neural model and some of the pre-rained models has been done. The results show that images were correctly identified with the best accuracy of 97.08% using one of the pre-trained convolutional neural networks based residual network models. The results of this work will be helpful in guiding the future researches in this novel application of thermal imaging in pothole detection field.