Syed Ibrahim Hassan Alias Sohail Abbas

The condition assessment of road surface (pavements) is a crucial task in order to ensure their usability and provide maximum safety for the public. It also allows the government to assign the limited resources for maintenance and consider long-term investment schemes. Pavement defects vary depending on the pavement surface. Pavement defects include cracking caused by failure of the surface layer, surface deformation such as rutting that results from weakness in one or more layers of the pavement, disintegrations such as potholes caused by progressive breaking up of pavement into small loose pieces and surface defects such as ravelling caused by errors during construction such as insufficient adhesion between the asphalt and aggregate particulate materials. Currently the road inspection is performed by the manual visual inspection where the structural engineers or certified inspectors manually assess the road condition. However, manual visual inspection is time consuming and cost-intensive. Over the last decade numerous technologies such as machine learning and computer vision have been applied for the assessment of road conditions such as cracks, potholes etc. An automated road cracks/defects detection and classification system could become a valuable tool for improving the performance and accuracy of the inspection and assessment process. Such a system could be used to evaluate the recorded images/videos to extract road condition data. An automated defect/cracks detection system could be integrated into existing road inspection tools to support the inspection process by providing real-time feedback and alerting the operator through highlighting the road defects, thus avoiding possible misinterpretation or missing defects due to operator fatigue. The aim of this research is to develop a machine learning approach to support automated detection/classification and segmentation of pavement defects using road image/videos obtained from various image acquisition devices.