Abstract
Automatic detection of potholes for pavement condition assessment leads to great savings in expenses and allows a better spending of resources destined to road infrastructure management. Out of all the available pothole detection techniques, the use of image-based methods within neural networks frameworks is the one that has offered the best balance between cost and accuracy. Convolutional Neural Networks (CNN) are deep neural network models specially designed for image processing problems. They have achieved remarkable results in many applications. Inspired by the success of such models, in this paper, we have evaluated the performance of 8 CNN methods in the task of pothole detection from pavement images. Along with the evaluation of the CNN architectures, we have also tested the impact of using pre-trained models with fine tuning procedures and data augmentation techniques. Such procedures enable the use of fewer training images. Given the high cost and labor intensiveness of obtaining labeled images, the annotated images were intentionally randomly selected to reduce the dataset from its original size. The dataset of images is from Brazilian roads acquired from a typical setup of a camera attached to a vehicle. The results indicate that pre-trained CNN models with fine tuning constitute a promising technique for pothole detection in pavements, especially when a large amount of labeled data is not available.