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This research paper presents a novel approach to pothole detection using DeepLearning and Image Processing techniques. The proposed system leverages theVGG16 model for feature extraction and utilizes a custom Siamese network withtriplet loss, referred to as RoadScan. The system aims to address the criticalissue of potholes on roads, which pose significant risks to road users.Accidents due to potholes on the roads have led to numerous accidents. Althoughit is necessary to completely remove potholes, it is a time-consuming process.Hence, a general road user should be able to detect potholes from a safedistance in order to avoid damage. Existing methods for pothole detectionheavily rely on object detection algorithms which tend to have a high chance offailure owing to the similarity in structures and textures of a road and apothole. Additionally, these systems utilize millions of parameters therebymaking the model difficult to use in small-scale applications for the generalcitizen. By analyzing diverse image processing methods and varioushigh-performing networks, the proposed model achieves remarkable performance inaccurately detecting potholes. Evaluation metrics such as accuracy, EER,precision, recall, and AUROC validate the effectiveness of the system.Additionally, the proposed model demonstrates computational efficiency andcost-effectiveness by utilizing fewer parameters and data for training. Theresearch highlights the importance of technology in the transportation sectorand its potential to enhance road safety and convenience. The network proposedin this model performs with a 96.12 % accuracy, 3.89 % EER, and a 0.988 AUROCvalue, which is highly competitive with other state-of-the-art works.

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