Magnetic flux leakage (MFL) detection has been widely used in the pipeline integrity evaluation. However, the MFL signals will be distorted due to the speed effect, which reduces the accuracy of the defect size inversion severely. To solve above problems, a multiple information-guided collaborative network (MIG-CN) is proposed to invert defect sizes accurately for the distorted pipeline MFL signals caused by the speed effect. First, a multiview feature fusion method is proposed, which enables MIG-CN to predict the defect sizes of specific types accurately by fusing the features of specific views, where the mechanism features are injected into the network to fully exploit the physical information of the MFL signals, improving the inversion accuracy. Second, a physics-informed constrained network is proposed, in which a speed magnetic dipole model is developed to impose constraints from real physical laws with the speed effect, thus enhancing the physical plausibility of the inversion results. Third, a dynamic-static collaborative decision strategy is proposed, in which a two-point constrained quantile probability modelling is proposed to model the inversion results of the undistorted MFL signals and obtain the prior probability distribution. The accuracy and robustness of the inversion results at different speeds are improved by minimizing the difference between the probability distribution of the inversion results of the distorted MFL signals and those of the undistorted. The above three components are jointly trained and optimized to improve the inversion accuracy under the influence of speed effect. Finally, the experiments on real pipeline networks verifies the effectiveness of MIG-CN.

Freespace detection plays an important role in autonomous driving. In recent years, deep learning based freespace detection methods have performed well in urban scenes. However, for off-road scenes, freespace detection poses significant challenges due to the complexity of the scenes and the lack of clear edges. The existing methods have not effectively fused LiDAR data and camera images. In this paper, we propose a Pyramid Cross-Modal Feature Fusion Network (PCMF2-Net) for off-road freespace detection. The dense depth maps are concatenated with RGB images and used as input along with surface normal maps. The dual branch CNN-Transformer encoder combines convolutional neural networks and transformers to extract local and global features from RGBD images and surface normal maps, respectively. Then, in the pyramid cross-modal feature fusion module, the multi-scale and multimodal encoder features are fused in a top-down manner. In addition, we also use an edge segmentation task and a two-step training strategy to further improve performance. Experiments on the off-road freespace detection dataset (ORFD) demonstrate that the proposed PCMF2-Net achieves a competitive result of 93.9% IoU at a speed of 23 Hz.

Generalizable neural implicit surface reconstruction aims to recover accurate surfaces with sparse views from unseen scenes. Most existing methods suffer from severe incompleteness and inaccuracies in the case of reconstruction with large viewpoint variations, as significant perspective distortions across views lead to unreliable feature correspondence and geometry representations. In this paper, we propose a cross-view geometric collaboration framework for generalizable neural surface reconstruction, which exploits cross-view complementary geometric information to improve the accuracy and robustness of reconstruction from sparse views. Specifically, we propose a cross-view geometry complement module that utilizes the reliable geometric information of different views to refine geometric representations. In addition, we construct a distortion-robust patch-based consistency volume to provide supplementary geometric cues for uncertain regions. For the rendering process, we develop a cross-view geometry transformer to adaptively aggregate reliable cross-view point features by considering geometric context along the ray. Finally, we render per-view depth maps and fuse them to reconstruct the final surface. Extensive experimental results on the DTU, BlendedMVS, and Tanks and Temples datasets demonstrate the superior reconstruction quality and view-combination generalizability of our solution.

Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on daytime benchmarks but struggle in nighttime scenarios due to limited visibility and challenging lighting conditions. To address these challenges, we propose LIAR, a novel framework that learns illumination-affined representations. LIAR first introduces Selective Low-light Image Enhancement (SLLIE), which leverages the illumination priors from daytime scenes to adaptively determine whether a nighttime image is genuinely dark or sufficiently well-lit, enabling more targeted global enhancement. Building on the illumination maps generated by SLLIE, LIAR further incorporates two illumination-aware components: 2D Illumination-guided Sampling (2D-IGS) and 3D Illumination-driven Projection (3D-IDP), to respectively tackle local underexposure and overexposure. Specifically, 2D-IGS modulates feature sampling positions according to illumination maps, assigning larger offsets to darker regions and smaller ones to brighter regions, thereby alleviating feature degradation in underexposed areas. Subsequently, 3D-IDP enhances semantic understanding in overexposed regions by constructing illumination intensity fields and supplying refined residual queries to the BEV context refinement process. Extensive experiments on both real and synthetic datasets demonstrate the superior performance of LIAR under challenging nighttime scenarios. The source code and pretrained models are available [here](https://github.com/yanzq95/LIAR).

Tracking and mapping in large-scale, unbounded outdoor environments using only monocular RGB input presents substantial challenges for existing SLAM systems. Traditional Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) SLAM methods are typically limited to small, bounded indoor settings. To overcome these challenges, we introduce GigaSLAM, the first NeRF/3DGS-based SLAM framework for kilometer-scale outdoor environments, as mainly demonstrated on the 4 kilometer-scale datasets. Our approach employs a hierarchical sparse voxel map representation, where Gaussians are decoded by neural networks at multiple levels of detail. This design enables efficient, scalable mapping and high-fidelity viewpoint rendering across expansive, unbounded scenes. For front-end tracking, GigaSLAM utilizes a metric depth model combined with epipolar geometry and PnP algorithms to accurately estimate poses, while incorporating a Bag-of-Words-based loop closure mechanism to maintain robust alignment over long trajectories. Consequently, GigaSLAM delivers high-precision tracking and visually faithful rendering on urban outdoor benchmarks, establishing a robust SLAM solution for large-scale, long-term scenarios, and significantly extending the applicability of Gaussian Splatting SLAM systems to unbounded outdoor environments.

Accurate incipient fault (IF) identification in distribution networks (DNs) is crucial for improving the reliability of industrial electricity. Previous studies made great efforts on massive data-driven IF identification methods. However, there is difficulty in extensive IF data recording and collection in engineering practice, as IF is not easy to trigger the action of relay protection devices. Furthermore, attempts to collect IF data from multiple DNs in a centralized manner to increase sample size seem theoretically feasible, but can become impractical due to data privacy concerns among power utilities in different administrative regions. Therefore, a pretraining method is designed to learn general knowledge about various faults in DNs, providing small sample learning ability to address the challenge of small samples of IF. Then, a federated learning-based distributed training framework is devised to protect the data privacy of power utilities in different regions in collaboration model training. In addition, the imbalance of IF data distribution in different DNs in engineering practice is embedded in model learning by an adaptive weighting factor$\omega ^{I}$to improve generalization performance. The above-mentioned contributions bring out the conception of the pretraining adaptive federated learning IF identification method (PTAFedIF). Case studies on DNs IF data show that PTAFedIF achieves 96.7$\%$identification accuracy in small sample conditions, significantly superior to traditional IF identification methods. The embedded$\omega ^{I}$in PTAFedIF improves the IF identification accuracy by 0.5$\%$.

Unmanned aerial vehicle (UAV) assisted wireless power transfer (WPT) enables the network to provide flexible and controllable energy supplement towards low-powered devices, which is considered as a promising technology in the future Internet of Things (IoT) networks. In this paper, the integrated communication and control (ICAC) for UAV assisted WPT is studied, aiming for mitigating the performance degradation caused by the WPT beam misalignment between the UAV and the low-power device. The optimal blocklength of the control signal is then obtained, in order to maximize the lower bound of the average energy harvesting power of the device by satisfying the control reliability constraint. Simulation results evaluate the performance of the ICAC system, which demonstrates that the WPT performance is a decrease-and-increase-then-decrease function with respect to the blocklength of the control signal. The performance of the ICAC system can be improved by adjusting the blocklength of the control signal.

Traditionally, road detection approaches mostly capitalize on RGB images, 3D LiDAR point cloud or their fusion. However, RGB camera is sensitive to light conditions, while LiDAR point cloud is sparse compared with dense image pixels. In this work, a new hybrid image dataset is provided for the task of road detection based on cameras. In this dataset, the hybrid images are acquired by an optically aligned hybrid imaging device, consisting of a far-infrared (FIR) imager and an RGB camera to output pixel-wise registration of thermal and RGB frames. Then we investigate on three methods based on fully convolutional neural network (F-CNN) to demonstrate the advantages by fusing RGB-FIR images in road detection. First, a middle-fusion based model is built, where the output feature maps of encoder branches from RGB and FIR images are directly concatenated into a single-fusion branch as the decoder. Next, the originally discarded layers after fusion operation for both RGB and FIR branches are recovered as the mimic branches to imitate the distributions of the fusion outputs, which constitutes an extended cross model (ECM). Moreover, the outputs of mimic branches at different scales are also used to imitate the corresponding outputs in the fusion branch, called a hierarchical cross model (HCM). The experimental results demonstrate the effectiveness and efficiency of our fusion strategies.

In this paper, we propose a road detection method with LiDAR-camera fusion in a novel conditional random field (CRF) framework to exploit both range and color information. In the LiDAR based part, a fast height-difference based scanning strategy is applied in the 2D LiDAR range-image domain and a dense road detection result in camera image domain can be obtained through geometric upsampling given the LiDAR-camera calibration parameters. In the camera based part, a fully convolutional network is applied in the camera image domain. Finally, we fuse the dense and binary road detection results from both LiDAR and camera in a single CRF framework. Experiments show that using a single thread of CPU, the proposed LiDAR based part can operate at a frequency of over 250Hz with sparse output in range image and 40Hz with dense result in camera image for the 64-beam Velodyne scanner. Our CRF fusion method achieves very promising road detection performance on the KITTI-Road dataset.

In this work, we propose a novel paradigm to design a hybrid thermal/EO (Electro-Optical or visible-light) camera, whose thermal and RGB frames are pixel-wisely aligned and temporally synchronized. Compared with the existing schemes, we innovate in three ways in order to make it more compact in dimension, and thus more practical and extendable for real-world applications. The first is a redesign of the structure layout of the thermal and EO cameras. The second is on obtaining a pixel-wise spatial registration of the thermal and RGB frames by a coarse mechanical adjustment and a fine alignment through a constant homography warping. The third innovation is on extending one single hybrid camera to a hybrid camera array, through which we can obtain wide-view spatially aligned thermal, RGB and disparity images simultaneously. The experimental results show that the average error of spatial-alignment of two image modalities can be less than one pixel.

In this paper, we propose a two-view fusion based convolutional neural network to estimate road areas in urban environments with LiDAR point clouds as input only. The proposed network takes two transformed LiDAR data representations, the LiDAR imageries and the camera-perspective maps, as inputs. It outputs pixel-wise road detection results in both the LiDAR's imagery view and the camera's perspective view simultaneously, in an end-to-end manner. To make better use of the data associations between two representations, we construct a novel mapping layer to transform features from the LiDAR's imagery view to the camera's perspective view in order to strengthen the road detection performance in the camera's perspective view. Experiments on the KITTI-Road dataset show that the proposed network can achieve the state-of-the-art performance among all LiDAR-only methods in real time.

In this paper, we propose to fuse the geometric information of a 3-D LiDAR and a monocular camera to detect the urban road region ahead of an autonomous vehicle. Our method takes advantage of both the high definition of 3-D LiDAR data and the continuity of road in image representation. First, we obtain an efficient representation of LiDAR data and an organized 2-D inverse depth map, by projecting the 3-D LiDAR points onto the camera's image plane. Through the new representation, we can acquire the intermediate representations of road scenes by extracting the vertical and horizontal histograms of the normalized inverse depth. The approximate road regions can be quickly estimated with both histogram-based schemes. To accurately find the road area, we propose a row and column scanning strategy in the approximate road region to refine the detected road area. We have carried out experiments on the public KITTI-Road benchmark, and have achieved one of the best performances among the LiDAR-based road detection methods without learning procedure.

This paper proposes a new method for detecting a road from a stereo pair of images. First, the horizon is accurately estimated by a robust, weighted-sampling RANSAC-like method in the improved v-disparity map. The vanishing point of the road region is located using both the horizon information and road flatness constraints. Then it is used as the source node of a weighted graph formed by the pixels of the left stereo-image and their adjacency relationships. The weight of each edge measures the inconsistency of adjacent pixels, and is computed using both the gray-scale and disparity information. Detecting road borders is thus reduced to finding two shortest paths from the source node to the bottom row of the image by the Dijkstra algorithm. The proposed method has been tested on 2621 image pairs of different road scenes from the KITTI dataset. Our experiments demonstrate that this training free approach detects horizon, vanishing point, and road region accurately and robustly, and compares favorably with the state of the art on the KITTI benchmark.

This paper proposes a new method for road detection based on a 3D LiDAR and a camera. First, the original LiDAR point cloud is re-organized in an ordered way to generate a LiDAR imagery. Then the flat region is extracted from the LiDAR imagery as the candidate road region. Next, a strategy of row- and column- scanning is given in the LiDAR imagery to detect a finer road region from the candidate region. To fuse the point cloud with image information, we transform the point cloud that corresponds to the above detected road region to the image space according to the calibration parameters between the LiDAR and camera. Then we give two image-guided diffusion schemes to conduct image segmentation of road area, respectively. Our experiments demonstrate that this training free approach detects the road region fast, accurately and robustly, and compares favorably with the state-of-the-art on the KITTI benchmark.

In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI data set. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point, and road regions very accurately and robustly. It can achieve promising performance.

In this paper, we propose a robust vanishing-point constrained lane detection method with a stereo-rig. This method can achieve promising detection performance for both straight and curved lanes without assuming any parametric lane model. First, we propose an accurate and efficient road vanishing point detection scheme based on the v-disparity and visual odometry techniques, where the v-disparity map can significantly reduce the searching space for vanishing point, and the visual odometry can benefit the vanishing point detection of both straight and curved roads. Next, we formulate the lane detection problem as a graph-search procedure, where a vanishing-point constrained Dijkstra shortest-path lane model is proposed to obtain a minimum-cost map. The two lane borders can be detected by finding two optimal paths which originate from the vanishing point to two cost-map derived terminal points, respectively. The proposed method has been tested on the KITTI and the Oxford RobotCar data sets and it works accurately and robustly on a variety of road scenes.

Face recognition with partial occlusion is one of the urgent and challenging problems in the pattern recognition research. Using the Alternating Direction Method of Multipliers (ADMM), the recently proposed nuclear norm based matrix regression model (NMR) has been shown a great potential in dealing with the structural noise. And yet, ADMM needs to bring into an auxiliary variable and only exploits the convexity of NMR. Compared with ADMM, the gradient based methods are simpler. To make use of these methods, this paper considers the Approximated NMR (ANMR) model. Utilizing the singular value shrinkage operator and strong convexity of ANMR, the dual problem of ANMR (DANMR) is derived and a crucial result is obtained: the primal optimal solution of ANMR can be converted as the matrix function associated with the dual optimal solution. Due to the differentiability of DANMR, an adaptive line search scheme is developed to solve it. This approach combines the advantages of the accelerated gradient technique and adaptive parameters updating strategy. Therefore, a convergence rate of O(1/N2) can be guaranteed. Experimental results show the superiority of the proposed algorithm over some existing methods.

In this paper, we propose a new feature-point based RGB-D visual odometry approach for estimating the relative camera motion from two consecutive frames. The approach differs from most feature-point based RGB-D visual odometry approaches in two key aspects: (1) we do not directly use point correspondences to compute relative motion, instead, we link each two distinct points to form a line segment, then utilize correspondences of the generated line segments to estimate relative motion; (2) considering the measurement noise of the RGB-D camera, we design a threshold technique to control the size of maximum clique. Several experiments on real-world dataset show that our method achieved improved accuracy when compared with other recent RGB-D based odometry methods.

Regression based method is a hot topic in the face recognition community and has achieved interesting results when dealing with well-aligned frontal face images. However, most of the existing regression analysis based methods are sensitive to pose variations. In this paper, we firstly introduce the orthogonal Procrustes problem (OPP), which is simple but effective, as a model to handle pose variations in two-dimensional face images. OPP seeks an optimal transformation between two images to correct the pose from one to the other. We integrate OPP into the regression model and propose the structural orthogonal Procrustes regression (SOPR) using the nuclear norm constraint on the error term to keep image's structural information. Moreover, a subject-wise strategy is adopted to address the problem that the gallery images may span over different poses. The proposed model is solved by an efficient iteratively reweighted algorithm and experimental results on popular face databases demonstrate the effectiveness of our method.

A linear regression-based method is a hot topic in face recognition community. Recently, sparse representation and collaborative representation-based classifiers for face recognition have been proposed and attracted great attention. However, most of the existing regression analysis-based methods are sensitive to pose variations. In this paper, we introduce the orthogonal Procrustes problem (OPP) as a model to handle pose variations existed in 2D face images. OPP seeks an optimal linear transformation between two images with different poses so as to make the transformed image best fits the other one. We integrate OPP into the regression model and propose the orthogonal Procrustes regression (OPR) model. To address the problem that the linear transformation is not suitable for handling highly non-linear pose variation, we further adopt a progressive strategy and propose the stacked OPR. As a practical framework, OPR can handle face alignment, pose correction, and face representation simultaneously. We optimize the proposed model via an efficient alternating iterative algorithm, and experimental results on three popular face databases, such as CMU PIE database, CMU Multi-PIE database, and LFW database, demonstrate the effectiveness of our proposed method.