首页|Guangxi University Reports Findings in Robotics (3d Vision Technologies for a Self-developed Structural External Crack Damage Recognition Robot)
Guangxi University Reports Findings in Robotics (3d Vision Technologies for a Self-developed Structural External Crack Damage Recognition Robot)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Robotics. According to news reporting out of Nanning, People's Republic of China, by NewsRx editors, research stated, "Persistent cracking and progressive damage can weaken the operational performance of structures such as bridges, dams, and concrete buildings. Consequently, research into automated, high-precision crack detection methods remains pivotal within the realm of structural health monitoring (SHM)." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), National Natural Science Foundation of Guangxi Province, China Postdoctoral Science Foundation, Systematic Project of Guangxi Key Laboratory of Disaster Prevention and Engineering Safety. Our news journalists obtained a quote from the research from Guangxi University, "Presently, scholars predominantly rely on two-dimensional (2D) image-based algorithms for crack detection. However, these methods commonly struggle to accurately locate the three-dimensional (3D) coordinates of cracks on large structures and to extract the 3D contours of cracks. To address this challenge, this study proposes an automated 3D crack detection system for structures based on high-precision Light Detection and Ranging (LiDAR) and camera fusion. Firstly, precise registration of images and LiDAR point clouds was achieved through accurate extrinsic calibration of the sensors. Secondly, the lightweight MobileNetV2_DeepLabV3 crack semantic segmentation network was employed to detect and locate cracks. Finally, by automatically guiding the robotic arm, an industry-standard depth camera was able to capture high-precision 3D information about the crack at close observation points. Compared with the existing studies, this study emphasizes the extraction of high-precision 3D crack features and verifies the validity of the method by comparing the measurement results with those of the traditional method, demonstrating a remarkable measurement accuracy reaching sub-millimeter levels (0.1 mm)."
NanningPeople's Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRoboticsTechnologyGuangxi University
NETL
NSTL
