Woo Hyun-Jung, Seo Dong-Min, Kim Min-Seok, Park Min-San, Hong Won-Hwa, Baek Seung-Chan
School of Architecture, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Korea.
Department of Architecture, Kyungil University, Gyeongsan 38428, Korea.
Sensors (Basel). 2022 Sep 5;22(17):6711. doi: 10.3390/s22176711.
Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24-84 mm and 8-48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95-91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.
基于无人机（UAV）的结构裂缝检测技术的积极研究已引起了相当大的关注。现有的大多数使用无人机进行裂缝定位的研究都在无人机上安装了全球定位系统（GPS）/惯性测量单元（IMU）以获取位置信息。当使用这种绝对位置信息时，多项研究证实无人机的定位误差会被反映出来，误差量级在几米左右。为了解决这些局限性，在本研究中，不使用绝对位置信息，而是利用无人机拍摄图像中物体之间的相对位置来定义裂缝的位置，以显著降低误差水平。通过航拍，共获取了97张图像。利用点云技术、图像拼接和单应性矩阵算法，确定了5条裂缝和3个参考物体。重要的是，通过现场测量对估计的相对位置值和地面真值进行的对比分析表明，在x方向和y方向上分别获得了24 - 84毫米和8 - 48毫米范围内的误差。此外，还确认了均方根误差（RMSE）在37.95 - 91.24毫米之间。未来，所提出的方法可用于补充和改进基础设施和设施视觉检查的传统方法。
