Summary The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down‐sampling of low‐level features and up‐sampling of deconvolution of high‐level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small‐scale target.

SUMMARY With the promotion of smart grid construction work, the use of high‐precision and high‐efficiency substation inspection robot has become the development trend of substation inspection. A multi‐scale feature fusion meter target detection algorithm is proposed to address the problems of low efficiency and susceptibility to surrounding environmental factors by the traditional manual meter reading method. Kinecct is used to acquire color images of substation meters with different backgrounds, light intensities, and angles to build a substation meter dataset. Based on the complementarity and correlation of multi‐scale features, an SSD target detection model with multi‐scale feature fusion is established, and the performance of the algorithm is tested on the constructed dataset, and comparative experiments are conducted to verify the effectiveness of the algorithm for target detection accuracy improvement.

Summary Target localization in unknown environment is one of the development directions of mobile robots. Simultaneous localization and mapping (SLAM) can be used to build maps in unknown environments, but it has the problem of poor readability and interactivity. In this article, target detection and SLAM are combined to search and locate the target by using rich RGBD images information. The determined position in the global map is conducive to the follow‐up operation of the target by mobile robots. By establishing a local dense point cloud map of the target object, the current state of the target object is directly displayed, the readability of the map is improved, and the disadvantages of difficult understanding of the global sparse map and slow construction of the global dense map are avoided. A target localization algorithm under the framework of yolov4 is designed to apply in the process of SLAM global mapping. Our works are helpful for obtaining positions of objects in three‐dimensional space. The experimental results show that the time‐consuming of this method in dense mapping is reduced by 50%–70%, and the number of point clouds is also reduced by 60%–70%.

Abstract The manipulator workspace is an essential element in the field of manipulator research and is of great significance for manipulator motion planning. However, little research has been conducted on dividing the manipulator workspace into working subspaces. No precise division method has been proposed; the inverse kinematics of multiple solutions in manipulator trajectory planning may also cause abrupt joint changes, thus affecting the planned trajectory. The article proposes a working subspace division method for all ball‐wrist 6DOF(degree‐of‐freedom) manipulators that satisfy the Piper criterion to address the above problems. The kinematic model of the manipulator is established, and the Jacobi matrix of the manipulator is obtained. The space of joints of the manipulator is divided into unique domains containing only single inverse kinematic solutions by means of singular trajectory lines when the determinant of the Jacobi matrix is zero; The solution from the joint space to the workspace is achieved by a nonlinear mapping, which completes the partitioning of the work subspace, and each work subspace contains only unique inverse kinematic solutions. When trajectory planning is carried out from the independent area of a single workspace to the overlapping area of multiple workspaces, selecting the inverse kinematic solution in a single working subspace can effectively avoid abrupt changes in the joints of the manipulator and trajectory misalignment caused by numerous inverse solution selection problems and make the planned trajectory smooth and consistent with the operational requirements of each scene.

Summary Instance segmentation is a challenging task that requires both instance‐level and pixel‐level prediction and it has a wide range of applications in autonomous driving, video analysis, scene understandingand so on. The currently dominant instance segmentation methods have excellent accuracy, but they are slow, and the processing speed will be even less satisfactory if the input is a large‐scale image. In order to improve the efficiency and accuracy of instance segmentation of large‐scale images, this article modifies the backbone network based on YOLACT network, adds a multi‐information fusion module and provides an improved BiFPN method to achieve multi‐scale feature fusion, while adding two branches to the first level detector RetinaNet to achieve instance segmentation. The network model is tested on Cityscapes dataset and the results of the experiments show that the improved instance segmentation network in this article improves the accuracy while ensuring the speed of segmentation. The optimized network model size was reduced by 17% compared to YOLACT, and the mAP, mAP50, and mAP75 were improved by 18.3%, 32.1%, and 24.6%, respectively.

This paper proposed a simulation method to assess the remote sensing imaging system by which we can get a clear and visible result in the form of image. It can assist the design and development at low-cost.