Deep Learning-Based System for Monitoring Pavement Distresses

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Maintaining highway and airport pavement quality at an acceptable level is challenging for transportation authorities. Manual, semi-automated and automated methods have been developed to evaluate pavement distresses, such as cracks, viscoplastic deformation, and surface defects. However, the manual process is costly, labour-intensive, and time-consuming and is prone to human error. The semi-automated and automated methods lack accuracy and involve high costs. Furthermore, road and airport authorities lack the screening tools to make real-time evaluations associated with simultaneous visualizations. Our proposed automated vision-based system overcomes these limitations by detecting and evaluating all pavement distresses mentioned above more efficiently and accurately. The pavement distress monitoring system includes two parts: hardware and software. The hardware is an off-the-shelf camera with certain features used for image data collection. The software is a Python-based code that simultaneously analyzes the collected images using computer vision, image processing, and deep learning algorithms to detect and assess pavement distresses. The generated outputs include pavement distress type, dimensions, severity, and location. The proposed product can be installed at the back of a moving vehicle or on a drone. It can capture images remotely at different moving speeds and accomplishes the assessment process with reasonable cost and without traffic interruption. In addition, this technology can scan all road lanes by capturing successive images with just one passage along the road of interest and can be used during both day and nighttime. Based on our primary market size estimation, the serviceable available market is about 60,000 municipalities, airports, and provincial governments, and capturing 10% of this market amounts to 6,000 sales. Thus, given typical product values, the annual income might reach US$21M. Using the NSERC fund, we will work on our product market assessment to validate our business model elements and create a more realistic and reasonable development process in product specifications and capabilities from the customer's point of view.