Infrared (IR) optical gas imaging (OGI) is widely adopted in industrial environments for detecting fugitive gas emissions. However, conventional IR OGI systems rely heavily on manual inspection, lacking capabilities for active leak localization and in-depth analysis, which increases labor costs and risks of human error. To address these challenges, we present LeakHunter, an intelligent multitask framework designed for industrial gas leak monitoring and decision support. LeakHunter integrates seamlessly with IR cameras and can be deployed on edge computing devices, enabling real-time, on-site leak detection in harsh industrial settings. At the core of LeakHunter is a novel keypoint detection paradigm tailored for IR OGI, capable of localizing both leak sources and diffusion endpoints to enable effective spatiotemporal trend analysis. The framework also estimates critical leak attributes, including plume morphology and flow rate, supporting rapid and informed response. To further enhance detection accuracy, we introduce a biomimetic attention module that improves gas-background separation under complex thermal conditions, and a collaborative multitask head for efficient cross-task feature sharing. In addition, two benchmark datasets are proposed, one of which is a field-test set collected in real industrial scenarios. Experiments demonstrate that LeakHunter achieves state-of-the-art performance across multiple tasks, with an F2 score of 94.8% for gas segmentation and 97.9% for leak keypoint localization, while running at 28.4 FPS on a portable IR OGI device. These results highlight its potential as a deployable, intelligent solution for enhancing industrial safety and automation.

High DoF mobile manipulation of robots is a nonlinear, nonchain redundant problem. In this article, we focus on two subissues of robot mobile manipulation: whole-body inverse kinematics (whole-body IK) and operation-oriented motion planning (OOMP). Whole-body IK solves the robot arm joint configuration and the mobile base position configuration according to the target pose. OOMP generates a feasible trajectory from the current pose to the target pose. The trajectory can avoid obstacles and touch operated objects. We introduce neural network optimization (NNO) methods with two variations to solve whole-body IK and OOMP, respectively. For whole-body IK, we design a fully connected network (FCN) to predict ten DoF of position and joint configurations based on the target pose. We use these ten DoF configurations to derive the predicted pose for online optimization. For OOMP, we design a GRU-based network to generate trajectories based on the initial and goal states. We mainly adopt sphere masks to modify the point cloud properties of the target object dynamically. During optimization, the trajectory keeps away from point clouds but approaches sphere masks. Finally, we conduct extensive experiments both on a Franka Panda robot and a mobile dual-arm robot. The results demonstrate the superior performance of our NNO method on whole body IK and OOMP, and implement mobile manipulation in different environments successfully.

TheSixdegrees of freedom 6DoF pose estimation of texture-less objects provides a spatial understanding of industrial scenes and is the basis for accurate object manipulation. Recent studies have shown that the introduction of known model information and initial poses helps CNNs-based methods achieve better performance in complex scenes. However, the mapping from image space to pose space learned by neural networks is dimensionally lifting. Due to the lack of depth information, it is difficult for neural networks to capture local clues on texture-less surfaces and directly regress the relative 3-D translation and 3-D rotation. Instead, we propose a novel framework named REG-Net, which transforms the 6DoF pose estimation task into a 2-D keypoint registration problem. The proposed network first encodes regional prior information using multi-representation, utilizes the globally-consistent offset attention module to align 2-D keypoint features in a long range, and then estimates offsets and potential regions of keypoints. The proposed regional PnP simultaneously adjusts the keypoint locations in a short range and outputs the pose. This framework compresses the learning space of the network from 3-D to 2-D. Extensive experiments on two benchmark datasets demonstrate the robustness and accuracy of REG-Net. We further demonstrate the effectiveness of REG-Net in the reflective industrial part grasping applications.

Pose estimation is an essential technology for industrial robots to perform precise gripping and assembly. The state-of-the-art deep learning-based approach uses an indirect strategy, i.e., first finding local correspondence between the 2-D image and 3-D model, and then using the perspective-n-point and RANSAC methods to calculate the poses of ordinary objects. However, the metal parts in industry are reflective and textureless, making it difficult to identify distinguishable point features to establish 2-D–3-D correspondences. To address this problem, in this article, we propose a novel deep learning based two-stage method for pose estimation of reflective textureless metal parts, which accurately estimates the target pose using monocular red green blue (RGB) images. Since contours play an important role in both keypoints prediction and pose estimation stages, our method is named ContourPose. First, an additional contour decoder is adopted to implicitly constrain the keypoints prediction in the former stage, which improves the accuracy of the keypoints prediction. Then, the predicted contour of the previous stage is taken as geometric prior that is used to iteratively solve for the optimal pose. Experiments indicate that the proposed approach for reflective textureless metal parts has a significant improvement over the state-of-the-art approaches.

A major obstacle in digital projector technology is that images projected onto nonideal surfaces with large depth variances can easily become blurred. In this article, present a method to overcome projection defocus for projection surfaces that inevitably have complex shapes and large depth variances. The proposed method has two main advantages over traditional methods. First, an edge-intensification-based defocus compensation algorithm is proposed to manipulate the input image to compensate for its projection defocus before blurring occurs. Unlike previous time-consuming compensation algorithms, the proposed algorithm has very high efficiency, as it is noniterative and open loop. Second, a sinusoidal-projection-based estimation method is proposed to reduce kernel estimation errors on complex surface types. Unlike previous methods limited to specific surface types, the proposed method can provide consistently good kernel estimation results even for discontinuous and textured (nonpure white) projection surfaces. Hence, the proposed method can be applied to a wider range of applicable surfaces. These two contributions are demonstrated through extensive experiments and compared with the state-of-the-art methods.

Image compression is an intensively studied subject in computer vision. The deep generative model, especially generative adversarial networks (GANs), is a popular new direction for this subject. In this study, the authors propose a new compression method based on a generative model and focus on its application by GANs. The decoder in the proposed method is modified from the GAN generator model, which can produce visually real‐like synthetic images. It is one of the two models in GANs, which is trained through a two‐players' contest game. The encoder is an optimisation algorithm called backpropagation‐to‐the‐input, which derives from an image inpainting algorithm based on generative models. In the proposed method, the authors turn the encoding process into an optimisation task to search for optimal encoded representations. Compared with traditional methods, the proposed method can compress images from certain domains into extremely small and shape‐fixed encoded space but still retain better visual representations. It is easy and convenient to apply without any retraining or additional modification to the generative models.

Here, the authors present multistage estimation model embedding with structured pose prior (SPP), a novel coarse‐to‐fine framework for real‐time 3D hand estimation from single depth image. Authors’ main contributions can be summarised as follows: (i) The authors proposed SPP to enforce constraints of canonical hand pose instead of original hand pose. (ii) The authors are the first to adopt under‐complete stacked denoising auto‐encoder (SDA) to construct pose prior by mapping canonical hand pose to latent representation. In the case of enforcing constraints of canonical hand pose, the authors empirically validate that under‐complete SDA outperforms over‐complete SDA in improving the hand estimation accuracy. (iii) The authors propose candidate keypoints patches (CKP) as intermediate data to conduct further hand pose refinement. Experimental evaluation on two publically available datasets shows that authors’ model is competitive both in accuracy and computation time. Especially, authors’ method placed first in the location of palm key‐point on both two datasets, and the high accuracy of hand palm key‐point plays an important role in many applications, such as that manipulator can grasp objects to specific coordinates with the guiding of human hand palm.

This paper proposes a novel algorithm for decoding real-field codes over erroneous channels, where the encoded message is corrupted by sparse errors, i.e., impulsive noise. The main problem of decoding such a corrupted encoded message is to reconstruct the error vector; recently, a common way to reconstruct it is to find the sparsest solution to an underdetermined system that is constructed using a paritycheck matrix. Unlike the conventional approaches reconstructing the high-dimensional error vector directly, the proposed method crossly recovers the elements of error vector from two (or several) groups of low-dimensional equations. Compared with the traditional algorithms, the proposed method can decode an encoded message with a much higher corruption rate. Furthermore, the complexity of our method is linear, which is much lower than those of the traditional methods. The experimental results verified the high error correction ability and speed of the proposed method.

There exist two main problems in currently existing measurement matrices for compressed sensing of natural images, the difficulty of hardware implementation and low sensing efficiency. In this paper, we present a novel simple and efficient measurement matrix, Binary Permuted Block Diagonal (BPBD) matrix. The BPBD matrix is binary and highly sparse (all but one or several “1”s in each column are “0”s). Therefore, it can simplify the compressed sensing procedure dramatically. The proposed measurement matrix has the following advantages, which cannot be entirely satisfied by existing measurement matrices. (1) It has easy hardware implementation because of the binary elements; (2) It has high sensing efficiency because of the highly sparse structure; (3) It is incoherent with different popular sparsity basis' like wavelet basis and gradient basis; (4) It provides fast and nearly optimal reconstructions. Moreover, the simulation results demonstrate the advantages of the proposed measurement matrix.