Camera Based Road Anomaly Detection Using Deep Learning

With the focus on achieving high productivity and efficiency with the help of fast, safe, and reliable transportation, wide and well-maintained roads are required. Road anomalies such as potholes, water puddles, and cracks can pose a significant threat to the safety of motorists and pedestrians. Real-time detection of road anomalies is crucial for saving time and resources. In this paper, we propose a novel, real-time, deep learning-based framework for detecting and classifying road anomalies from images captured from a dashboard camera. These anomalies can be potholes, water puddles, patches, etc., detected using a deep learning model that performs object detection with high accuracy and speed. Our model is trained on novel data collected in unstructured environments, which gives a high level of accuracy despite the variations. Our framework can also be applied for various applications to ease road maintenance and increase road safety.