Department of Computer Software, Catholic University of Daegu, Gyeongsan-si 712010, Republic of Korea.
School of Computer Software, Catholic University of Daegu, Gyeongsan-si 712010, Republic of Korea.
Sensors (Basel). 2023 Jun 24;23(13):5851. doi: 10.3390/s23135851.
In this study, we propose an algorithm to improve the accuracy of tiny object segmentation for precise pothole detection on asphalt pavements. The approach comprises a three-step process: MOED, VAPOR, and Exception Processing, designed to extract pothole edges, validate the results, and manage detected abnormalities. The proposed algorithm addresses the limitations of previous methods and offers several advantages, including wider coverage. We experimentally evaluated the performance of the proposed algorithm by filming roads in various regions of South Korea using a UAV at high altitudes of 30-70 m. The results show that our algorithm outperforms previous methods in terms of instance segmentation performance for small objects such as potholes. Our study offers a practical and efficient solution for pothole detection and contributes to road safety maintenance and monitoring.
在这项研究中，我们提出了一种算法，以提高微小物体分割的准确性，从而精确检测沥青路面上的坑洼。该方法包括三个步骤：MOED、VAPOR 和异常处理，旨在提取坑洼边缘、验证结果和管理检测到的异常。所提出的算法解决了以前方法的局限性，并具有几个优点，包括更广泛的覆盖范围。我们通过使用 UAV 在 30-70 米的高空在韩国的各个地区拍摄道路，实验评估了所提出算法的性能。结果表明，在小物体（如坑洼）的实例分割性能方面，我们的算法优于以前的方法。我们的研究为坑洼检测提供了一种实用且高效的解决方案，并有助于道路安全维护和监测。
