Automated Road Damage Detection Framework Using Deep Learning Object Detection Models

Detection of road damages is critical for road safety and efficient road maintenance. In this article, we present deep learning solution for image pothole detection with 3 popular object recognition algorithms: Faster R-CNN, RetinaNet, and SSD300 with VGG16. It is done through preprocessing a map of road images labeled with the location of potholes and data enhancement to strengthen the model. Each model is tuned for pothole detection using modified detection heads depending on our dataset class classes using classification loss function and localization loss function with adaptive learning rate scheduling and early stopping to optimize. The performance is calculated as Mean Average Precision (mAP) at various IoU thresholds, with the ResNet model giving higher detection rates. This evidence shows the models' potential to predict potholes in real-time, resulting in improved road safety and preventive maintenance.