Rotated object detection aims to locate and classify objects with arbitrary orientations. In complex backgrounds, small rotated objects with limited salient features present challenges for standard backbones to extract high-quality, discriminative features. Additionally, traditional single-stage detection heads suffer from spatial prediction biases due to misalignment between classification and localization tasks. To tackle these challenges, We propose a novel network architecture, YOLO-KED, which incorporates the KAGFusion Module (KAGFM), EdgeFusion Module (EFM), and Dynamic Alignment and Rotated Detection Head (DARH) to enhance the feature extraction precision of the backbone and dynamically align the classification and localization tasks. We have also released a new dataset, termed as ICDM, specifically for rotated object detection, containing 3,214 images across 11 common categories of industrial components. Experimental results demonstrate that YOLOKED outperforms existing state-of-the-art methods across multiple detection metrics, especially in complex scenarios and small object detection tasks. ICDM is available at https://github.com/twodian/ICDM.

Accurate hand-eye calibration is crucial for robotics and computer vision tasks, such as robotic manipulation and 3D object recognition, as it directly affects operational precision and visual perception. However, existing methods rely solely on point clouds and neglect geometric features like plane normals, leading to suboptimal spatial alignment. Additionally, the lack of an intelligent view selection strategy worsens initial alignment errors, causing cumulative inaccuracies and degraded performance. We propose HCNV, a novel framework that combines optimized point cloud alignment with intelligent view selection to overcome these limitations. Our method uses plane normals and a hybrid genetic algorithm to refine the point cloud transformation matrix, significantly improving spatial alignment accuracy. Furthermore, the intelligent view selection strategy enhances point cloud matching and optimizes view coverage, increasing robustness across various conditions. Experimental results show that HCNV improves calibration accuracy by 20% and reduces computational time by 30% compared to state-of-the-art methods, demonstrating its effectiveness and practicality in real-world applications.

Six-dimensional (6D) pose estimation is an important branch in the field of robotics focused on enhancing the ability of robots to manipulate and grasp objects. The latest research trend in 6D pose estimation is to directly predict the positions of two-dimensional (2D) keypoints from a single red, green, and blue (RGB) image through convolutional neural networks (CNNs) and establish a corresponding relationship with the three-dimensional (3D) keypoints of the model. Then, the perspective-n-point (PnP) algorithm is used to recover the 6D pose parameters. Currently, two challenges are encountered in pose estimation based on an RGB image. On the one hand, an RGB image lacks depth information, and it is thus difficult to directly obtain the corresponding geometric object information. On the other hand, when depth information is available, it is difficult to efficiently fuse the features of the RGB image with the features of the corresponding depth image. In this paper, we propose a bidirectional depth residual fusion network with a depth prediction (DP) network to estimate the 6D poses of objects (BDR6D). The BDR6D network predicts the depth information of objects using an RGB image, converts the depth information into point cloud information, and performs feature extraction and representation together with the RGB information during the feature extraction and representation stages. Specifically, the RGB image is fed into the BDR6D network, the DP network predicts the depth information of the objects in the image, and the depth map and RGB image are input into a point cloud network (PCN) and CNN, respectively, for feature extraction and representation. We build the bidirectional depth residual (BDR) structure so that the CNN and PCN can share information during feature extraction and representation. This approach allows the two networks to use each other’s local and global information to improve feature extraction and representation. For the keypoint selection stage, we propose an effective 2D keypoint selection method that considers the appearance and geometric information of the object of interest. We evaluate the proposed method with three benchmark datasets and compare it with other 6D pose estimation algorithms. The experimental results show that our method outperforms the state-of-the-art approach. Finally, we deploy our proposed method in conjunction with the Universal Robots 5 manipulator (UR5) robot to grasp and manipulate objects.Note to Practitioners—The purpose of this paper is to solve the problem of 6D pose estimation for robot grasping. The existing RGB image-based pose estimation approach faces two challenges. On the one hand, a single RGB image lacks depth information, so that it is difficult to directly obtain the corresponding geometric object information. On the other hand, when depth information is available, it is difficult to efficiently fuse the features of the RGB image with the features of the corresponding depth image. To solve the above problems, a novel network that can predict the depth information of objects from an RGB image and fuse the depth information with the RGB information to estimate the 6D pose of objects is proposed. Furthermore, we propose an effective 2D keypoint selection method that considers the appearance and geometric information of objects of interest. We evaluate the proposed approach based on three benchmark datasets and the UR5 robot platform and verify that our method is effective.

Category-level 6D pose and size estimation is to estimate the rotation, translation and size of the observed instance objects from an arbitrary angle in a cluttered scene. Compared with instance-level 6D pose estimation, there are two main challenges for category-level 6D pose estimation. One is that the algorithm needs to estimate the 6D pose and size of unseen objects, and no 3D models are available. Another is that different instance objects of the same class of objects differ greatly in shape. This paper propose a novel method to estimate the 6D pose and size of unseen objects from an RGB-D image. To handle intra-class shape variation, we propose an autoencoder-decoder that is trained on a set of object models to learn structural feature-invariant and shape-variant features of intra-class objects, and constructs a category-level priori model containing the structure feature and shape feature. To solve the problem of 3D model, this paper proposes a model reconstruction network including 3D graph convolution and spherical convolution (GSNet), which can reconstruct the 3D model of the observed instance object from the input RGB-D image and the priori model, and establish a dense correspon-dence between the 3D model and the observed instance object. Finally, random sample consensus (RANSAC) algorithm and Umeyama algorithm are used to estimate the 6D pose and size of the object. Extensive experiments on benchmark datasets show that the proposed method achieves state-of-the-art performance in category-level 6D object pose estimation. In order to prove that our method can be applied to the grasping and operation tasks of robots in industry and life, we deploy our method to a physical UR5 robot to perform grasping tasks on unseen but category known instances, and the results validate the efficacy of our proposed method.

Photometric differences are widely used as supervision signals to train neural networks for estimating depth and camera pose from unlabeled monocular videos. However, this approach is detrimental for model optimization because occlusions and moving objects in a scene violate the underlying static scenario assumption. In addition, pixels in textureless regions or less discriminative pixels hinder model training. To address these problems, in this paper, we deal with moving objects and occlusions by utilizing the differences between the flow fields, and the differences between the depth structure generated by affine transformation and view synthesis, respectively. Secondly, we mitigate the effect of textureless regions on model optimization by measuring the differences between features with more semantic and contextual information without requiring additional networks. In addition, although the bidirectionality component is used in each sub-objective function, a pair of images is reasoned about only once, which helps reduce overhead. Extensive experiments and visual analysis demonstrate the effectiveness of the proposed method, which outperforms existing state-of-the-art self-supervised methods under the same conditions and without introducing additional auxiliary information.

6D pose estimation of objects is an important part of robot grasping. The latest research trend on 6D pose estimation is to train a deep neural network to directly predict the 2D projection position of the 3D key points from the image, establish the corresponding relationship, and finally use Pespective-n-Point (PnP) algorithm performs pose estimation. The current challenge of pose estimation is that when the object texture-less, occluded and scene clutter, the detection accuracy will be reduced, and most of the existing algorithm models are large and cannot take the real-time requirements. In this paper, we introduce a Multi-directional Feature Pyramid Network, MFPN, which can efficiently integrate and utilize features. We combined the Cross Stage Partial Network (CSPNet) with MFPN to design a new network for 6D pose estimation, MFPN-6D. At the same time, we propose a new confidence calculation method for object pose estimation, which can fully consider spatial information and plane information. At last, we tested our method on the LINEMOD and Occluded-LINEMOD datasets. The experimental results demonstrate that our algorithm is robust to textureless materials and occlusion, while running more efficiently compared to other methods.

As more and more distributed power is integrated into the micro grid, fault detection and localization become more complex. This paper considers the integrity of fault detection and the accuracy of positioning, as well as the economic cost of implementation. In this paper, the distributed decision for the protection of the micro-grid based on two measured by PMU voltage and current on both ends of the line, calculate the equivalent impedance of each line, when the fault occurs, each PMU only communication two PMU which they adjacent. By judging the change of positive impedance value of the upstream and downstream to judge whether the fault occurs and its line. Then the fault line based on two terminal location method for accurate positioning. This method overcomes the problems of incomplete fault detection, large positioning error and high cost. This algorithm is suitable for radial and circumferential two kinds of micro grid, and equally effective in operation of islanding and grid. Finally, in order to verify the method, several simulations have been undertaken using SIMULINK software packages.