Feasibility Study for an Automatic Architecture for Pothole Detection in Asphalt Images: a Trade-off between Performance and Quality

摘要

This paper presents a computational architecture to detect potholes in the asphalt images, classifying images into two sets: those that contain potholes and those that do not contain potholes. Two different classifiers – Artificial Neural Network (ANN) and Support Vector Machine (SVM) – were evaluated and their results compared against predefined metrics – accuracy, precision, recall and F-score – and also training and processing times. In our approach, in contrast to other works, we explore how parameter setting in classifiers influences the performance and the outcome quality. This feature is very important to assess the applicability of the solution in a real time system. Overall, we observed that SVM classifier allows better results than ANN, providing adequate rates across all metrics and evaluated configurations. On the other hand, ANN classifier requires more time for training, although the cost of training is not a concern in the recognition architecture with regard to the feasibility of real time application.