Enhancing Pavement Evaluation Through Robotics Technology
India has the second-largest road network in the world, spanning a total of 5.89 million kilometers. This road network transports 64.5% of all goods in the country and 90% of India’s total passenger traffic uses the road network to commute. Retrieval of road information, such as road surface material and pavement type condition, is one of the essential issues in urban areas. Traditional evaluation methods are time-consuming and less accurate. Also, the interpretation of the severity of the damage is determined based on the judgment; these results can be subjective and may vary. Integrating more automated and semi-automated is inevitable and may provide valuable results when generated/interpreted by a computer. Robotic tool help with visualization and data collection. This research provides an Autonomous Robot System (ARS) to perform pavement assessments. ARS will gather video/image streams to be processed on a server with a pre-training Convolutional Neural Network (CNN) that can recognize crack existence. The outcome of these surveys is likely to be less expensive, more consistent, and faster and can cover wider areas.
