Xu Zhen, Wang Yingwang, Hao Xintian, Fan Jingjing
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Sensors (Basel). 2023 Jul 10;23(14):6271. doi: 10.3390/s23146271.
The current method of crack detection in bridges using unmanned aerial vehicles (UAVs) relies heavily on acquiring local images of bridge concrete components, making image acquisition inefficient. To address this, we propose a crack detection method that utilizes large-scene images acquired by a UAV. First, our approach involves designing a UAV-based scheme for acquiring large-scene images of bridges, followed by processing these images using a background denoising algorithm. Subsequently, we use a maximum crack width calculation algorithm that is based on the region of interest and the maximum inscribed circle. Finally, we applied the method to a typical reinforced concrete bridge. The results show that the large-scene images are only 1/9-1/22 of the local images for this bridge, which significantly improves detection efficiency. Moreover, the accuracy of the crack detection can reach up to 93.4%.
当前利用无人机（UAV）进行桥梁裂缝检测的方法严重依赖于获取桥梁混凝土构件的局部图像，导致图像采集效率低下。为了解决这个问题，我们提出了一种利用无人机获取的大场景图像的裂缝检测方法。首先，我们的方法包括设计一种基于无人机的方案来获取桥梁的大场景图像，然后使用背景去噪算法处理这些图像。随后，我们使用一种基于感兴趣区域和最大内切圆的最大裂缝宽度计算算法。最后，我们将该方法应用于一座典型的钢筋混凝土桥梁。结果表明，对于这座桥梁，大场景图像仅为局部图像的1/9至1/22，这显著提高了检测效率。此外，裂缝检测的准确率可达93.4%。
