Smart dimensional quality assessment of embedded steel plates based on images and laser data fusion

Abstract

Abstract Accurate and efficient positioning is critical to ensuring the dimensional quality assessment of embedded steel plates. However, traditional manual measurement methods struggle to efficiently measure and evaluate these plates. Vision-based measurement methods offer advantages such as high resolution, fast data acquisition, and processing speed, allowing accurate measurement of 2D coordinates. LiDAR can capture highly accurate point clouds, due to the unordered nature of point clouds, processing and analysis require significant computational resources. This paper proposes a method for smart 3D localization of embedded steel plates using image and laser data. (1) We introduce an improved Rectangular Diagonal Constraint Harris (RDC-Harris) corner detection method and achieve subpixel 2D corner detection of embedded plates based on deep learning；(2) Given a calibrated camera-LiDAR, we develop a smart detection algorithm guided by 2D image bounding boxes, achieving 3D corner localization. In indoor testing and engineering applications, this method effectively ensures the dimensional quality of embedded steel plates. Compared to traditional manual inspection, the measurement efficiency reaches 10 minutes per station, with an accuracy of 2.12 mm.