RoadNet: Efficient Model to Detect and Classify Road Damages

Abstract

Poorly maintained roads can cause lethal automobile accidents in various ways. Thus, detecting and reporting damaged parts of roads is one of the most crucial road maintenance tasks, and it is vital to identify the type and severity of the damage to help fix it as soon as possible. Several researchers have used computer vision and detection algorithms to detect and classify road damages, including cracking, distortion, and disintegration. Providing automatic road damage detection methods can help municipalities save time and effort and speed up maintenance operations. This study proposes a method to classify road damage and its severity based on CNN and trained on a newly curated dataset collected from Saudi roads. Hence, this study also presents a dataset with labeled classes, which are cracks, potholes, depressions, and shoving. The dataset was collected in collaboration with maintenance employees in the municipality of Rabigh Governorate using a smartphone device and reviewed by experts. In addition, several deep learning algorithms were implemented and evaluated using the proposed dataset. The study found that the proposed custom CNN (RoadNet) has higher accuracy than pre-trained models.

Keywords

• deep learning
• convolution neural network
• road damage
• cracks
• potholes
• transfer learning