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.

中文翻译：

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使用卷积神经网络和无人机的基于图像的道路裂缝风险评估

人们普遍认为，快速裂缝评估对于在适当条件下监测和维护道路至关重要。在本文中，提出了一种新的受裂缝影响的风险知情评估框架，用于道路的监测和维护。该框架包括五个步骤：数据收集、裂缝检测、裂缝位置提取、裂缝实际尺寸计算和风险等级评估。为了支持该框架，无人机 (UAV) 用于监控道路和收集数据。最先进的语义分割网络 DeepLabv3+ 也用于检测裂缝。根据检测到的裂纹的高度和像素统计，利用像素物理转换系数方程可以计算出裂纹的真实尺寸。随后对道路进行风险评估，确定裂缝位置，以便确定维修优先级。在真实道路上使用无人机进行数据收集和实验，以验证所提出方法的可行性和有效性。发现所提出的方法不仅能够从收集的图像中确定裂缝的真实大小，而且能够确定其风险等级。总之，该方法为可用于实际应用的风险告知道路裂缝评估提供了一种方便且可靠的解决方案。发现所提出的方法不仅能够从收集的图像中确定裂缝的真实大小，而且能够确定其风险等级。总之，该方法为可用于实际应用的风险告知道路裂缝评估提供了一种方便可靠的解决方案。发现所提出的方法不仅能够从收集的图像中确定裂缝的真实大小，而且能够确定其风险等级。总之，该方法为可用于实际应用的风险告知道路裂缝评估提供了一种方便且可靠的解决方案。