3-D Reconstruction for Thermal Infrared Images of Substation Equipment Based on Visual SLAM and Cascade MVSNet

A new method for 3-D reconstruction that combines visual simultaneous localization and mapping (V-SLAM) and Cascade multiview stereo network (MVSNet) is proposed in this article, which enables the direct reconstruction of the 3-D temperature field of substation equipment using video streams captured by a thermal infrared (TIR) imager. First, a V-SLAM-based pose estimation method is proposed to determine the position of the captured substation equipment. Parameter self-adjusted retinex (PSAR) enhancement algorithm and sparse texture feature matching (STFM) method are developed to solve the problem of difficult extraction of TIR image features with weak texture. The key frame selection method based on deep learning object detection is proposed to improve the efficiency of the depth computation. Second, the improved Cascade MVSNet is used to compute the depth maps at the key frames in a hierarchical manner, and the loss functions of the scale and perspective consistency are utilized to eliminate the reconstruction errors resulted from different sizes and mutual occlusion of substation devices. The experimental results illustrate that this method is capable of directly reconstructing the 3-D temperature field of a single substation device and a group of substation devices using TIR images, and the suggested algorithm is more advantageous in terms of accuracy, speed, and memory consumption compared with other state-of-the-art methods.