Deep Learning-Based pavement defect detection

Abstract

Pavement defects can significantly impact road safety, and detecting and repairing these defects is important. However, pavement defects detection by humans is time-consuming. With the advances in information and communication technology, many vehicles on the road are fitted with cameras, generating massive, crowdsourced data. This study demonstrates the usage of deep learning and computer vision to identify and classify pavement defects. We used the Road Damage Dataset 2022 (Arya et al., 2022) to train and test different object detectors, ensuring accurate and reliable detection. The initial results showed that it is possible to identify and classify pavement defects efficiently with results of 80% mAP50, reducing the risk of accidents, in addition, using these methods can lead to cost savings in maintenance and repair expenses, as well as reduce the environmental impact of routine road surveys.