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Abstract. Evaluation of surface distress is an important aspect of pavement management. The most common practice to assess surface distress is to develop a pavement condition index (PCI), with ASTM-PCI being the most widely used in evaluating flexible pavements. Traditional PCI evaluation methods rely on labour-intensive, manual inspections, leading to significant time consumption. In recent years, real-time visualization and crowdsourcing have been explored, but their potential has yet to be fully realized. Integrating real-time visualization through GIS technology offers immediate insights into pavement conditions, aiding prompt decision-making. Crowdsourcing allows a broader community to contribute to condition reporting, enhancing data accuracy and coverage. This study aims to develop an artificial intelligence (AI)-based method for road condition assessment from crowd-sourced images. A deep-learning object detection model is utilized for precise crack detection and classification. The model is trained to recognize various distress types accurately and quantify attributes crucial for determining the PCI. The developed model is then integrated into a web-based platform accessible through mobile phones and dash cameras, allowing real-time capture and classification of cracks. The study demonstrates that the automated methodology significantly enhances PCI estimation efficiency, with a high correlation between semi-automated and automated methods. Stakeholders can benefit from deep learning and automation in pavement distress detection, aiding informed decision-making through crowdsourcing data. Future work includes the detection of subclasses within crack types based on severity and the creation of digital twins for public assets. Overall, this study highlights the transformative potential of AI and crowdsourcing in improving pavement management.
Satheesan et al. (Thu,) studied this question.