Infrared noncontact detection has the advantages of having no power cut-off and convenient operation. However, when the SF ₆ (sulfur hexafluoride) gas leakage is small, the existing detection algorithms have low accuracy and are easily disturbed by noise. To solve this problem, this article proposes an online detection method for SF ₆ gas leakage based on a Gaussian mixture model. First, as a preprocessing stage, the improved dynamic interframe time domain filter is used to suppress the random noise in the image. Then, contrast limited adaptive histogram equalization (CLAHE) is used to enhance the dark details of the infrared image to improve the local contrast. Finally, the improved Gaussian mixture background model is used to adaptively segment the SF ₆ gas leakage and mark the leakage area. The results show that under the experimental conditions of 0.06 mL/min indoor and 5-m distance, the algorithm can overcome the high noise and complex background disturbances of infrared imaging. It can effectively detect and locate the SF ₆ leakage area and gets a higher F1-score compared with other conventional algorithm under the experimental conditions. In addition, when the resolution of the infrared image is 320 ×240, the running efficiency of the algorithm meets the real-time requirements.

Dynamic environments are challenging for visual Simultaneous Localization and Mapping, as dynamic elements can disrupt the camera pose estimation and thus reduce the reconstructed map accuracy. To solve this problem, this study proposes an approach for eliminating dynamic elements and reconstructing static background in indoor dynamic environments. To check out dynamic elements, the geometric residual is exploited, and the static background is obtained after removing the dynamic elements and repairing images. The camera pose is estimated based on the static background. Keyframes are then selected using randomized ferns, and loop closure detection and relocalization are performed according to the keyframes set. Finally, the 3D scene is reconstructed. The proposed method is tested on the TUM and BONN datasets, and the map reconstruction accuracy is experimentally demonstrated.

Infrared and visible images of the same scene are fused to produce a fused image with richer information. However, most current image-fusion algorithms suffer from insufficient edge information retention, weak feature representation, and poor contrast, halos, and artifacts, and can only be applied to a single scene. To address these issues, we propose a novel infrared and visual image fusion algorithm based on a bilateral–least-squares hybrid filter (DBLSF) with the least-squares and bilateral filter hybrid model (BLF-LS). The proposed algorithm utilizes the residual network ResNet50 and the adaptive fusion strategy of the structure tensor to fuse the base and detail layers of the filter decomposition, respectively. Experiments on 32 sets of images from the TNO image-fusion dataset show that, although our fusion algorithm sacrifices overall time efficiency, the Combination 1 approach can better preserve image edge information and image integrity; reduce the loss of source image features; suppress artifacts and halos; and compare favorably with other algorithms in terms of structural similarity, feature similarity, multiscale structural similarity, root mean square error, peak signal-to-noise ratio, and correlation coefficient by at least 2.71%, 1.86%, 0.09%, 0.46%, 0.24%, and 0.07%; and the proposed Combination 2 can effectively improve the contrast and edge features of the fused image and enrich the image detail information, with an average improvement of 37.42%, 26.40%, and 26.60% in the three metrics of average gradient, edge intensity, and spatial frequency compared with other algorithms.