RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous Pothole Detection in Roads

In the past few years, there have been numerous accidents due to potholes on the road. Many methods have been attempted to remove these potholes, but these methods are time-consuming. Hence, a driver should detect potholes from a safe distance in order to avoid damage. Existing methods for pothole detection heavily rely on object detection algorithms which tend to have a high chance of failure owing to the similarity in structures and textures of a road and a pothole. Additionally, these systems utilize millions of parameters thereby making the model difficult to use in small-scale applications for the general citizen. This research paper presents a novel approach to pothole detection using deep learning and image processing techniques. The proposed system leverages the VGG16 model for feature extraction and utilizes a custom Siamese network with triplet loss, referred to as RoadScan. Evaluation metrics such as accuracy, EER, precision, recall, and AUROC validate the effectiveness of the system. Additionally, the proposed model demonstrates computational efficiency and cost-effectiveness by utilizing fewer parameters and data for training. The network proposed in this model performs with a 96.12% accuracy, 3.89% EER, and a 0.988 AUROC value, which is highly competitive with other state-of-the-art works.