Image-based road crack risk-informed assessment using a convolutional neural network and an unmanned aerial vehicle

Rapid crack assessment is widely thought to be critical for monitoring and maintaining roads in appropriate conditions. In this paper, a novel crack-affected risk-informed assessment framework is proposed for the monitoring and maintenance of roads. The framework includes five steps: data collection, crack detection, crack location extraction, crack real-size calculation, and risk-level assessment. To support the framework, an unmanned aerial vehicle (UAV) is used to monitor roads and collect data. A state-of-the-art semantic segmentation network, DeepLabv3+, is also applied to detect cracks. Based on the height and pixel statistics of the detected crack, the real size of the crack can be calculated using the pixel-physical conversion coefficient equation. This is followed by a risk-informed assessment of the road identifying the location of the crack for maintenance priority determination. Data collection and experiments using a UAV were performed on a real road to verify the feasibility and effectiveness of the proposed method. It was found that the proposed method was not only able to determine the real size of the cracks from the collected images but also able to determine their risk levels. In summary, this method presents a convenient and reliable solution for risk-informed road crack assessment that can be employed in practical applications.