Det-Recon-Reg: An Intelligent Framework towards Automated Large-Scale Infrastructure Inspection

Visual inspection plays a predominant role in in- specting infrastructure surface. However, the generalization of existing visual inspection systems to large-scale real-world scenes remains challenging. In this paper, we introduce Det-Recon- Reg, an intelligent framework separating the complex inspection procedure into three stages: Detect, Reconstruct, and Register. (1) For defect detection (Detect), we present the first high- resolution defect dataset tailored for large-scale defect detection. Based on the dataset, we evaluate the most effective real-time object detection algorithms and push the boundary by proposing CUBIT-Net for real-world defect inspection. (2) For infrastruc- ture reconstruction (Reconstruct), we propose a learning-based multi-view stereo (MVS) network to adapt to large-scale scenes, taking as input the multi-view images and outputting the point cloud reconstruction, where its performance has been validated on the standard MVS datasets, including BlendedMVS, DTU, and Tanks and Temples datasets. (3) For defect localization (Register), we propose an effective registration method based on the geographic information system that registers the detected defects onto the reconstructed infrastructure model to establish a global reference for maintenance measures. The real-world experiments further verify the effectiveness and efficiency of our proposed framework. More details about our proposed dataset, code, and appendix are available on our project page: https://cuhk-usr-group.github.io/large-scale-inspect-framework/.