Kaihua Zhang

The intelligent detection of objects in remote sensing images has gradually become a research hotspot for experts from various countries, among which optical remote sensing images are considered to be the most important because of the rich feature information, such as the shape, texture and color, that they contain. Optical remote sensing image target detection is an important method for accomplishing tasks, such as land use, urban planning, traffic guidance, military monitoring and maritime rescue. In this paper, a multi stages feature pyramid network, namely the Multi-stage Feature Enhancement Pyramid Network (Multi-stage FEPN), is proposed, which can effectively solve the problems of blurring of small-scale targets and large scale variations of targets detected in optical remote sensing images. The Content-Aware Feature Up-Sampling (CAFUS) and Feature Enhancement Module (FEM) used in the network can perfectly solve the problem of fusion of adjacent-stages feature maps. Compared with several representative frameworks, the Multi-stage FEPN performs better in a range of common detection metrics, such as model accuracy and detection accuracy. The mAP reaches 0.9124, and the top-1 detection accuracy reaches 0.921 on NWPU VHR-10. The results demonstrate that Multi-stage FEPN provides a new solution for the intelligent detection of targets in optical remote sensing images.

With the rapid development of industry, people’s requirements for the functionality, stability, and safety of electronic products are becoming higher and higher. As an important medium for power supply and information transmission functions of electronic products, high-quality soldering of cables and connectors ensures that the devices can operate normally. In this paper, we propose a multi-level feature detection network based on multi-level feature maps fusion and feature enhancement for detecting connector solder joints, classifying and locating qualified solder joints, and detecting seven common defective solder joints. This paper proposes a new feature map up-sampling algorithm and introduces a feature enhancement module, which better preserves the semantic information of higher-level feature maps, while at the same time enhancing the fused feature maps and weakening the effect of noise. Through comparison experiments, the mAP of the network proposed in this paper reaches 0.929 and the top-1 accuracy reaches 92%. The detection capability of each type of solder joint is greatly improved compared with the effect of other networks, which can assist engineers in the detection of weld joint quality and thus reduce the workload.

The miniaturization and high integration of electronic products have higher and higher requirements for welding of internal components of electronic products. A welding quality detection method has always been one of the important research contents in the industry, among which, the research on solder joint defect detection of a connector has gradually attracted people’s attention with the development of image detection algorithm. The traditional solder joint detection method of connector adopts manual detection or automatic detection methods, which is inefficient and not safe enough. With the development of deep learning, the application of a deep convolutional neural network to target detection has become a research hotspot. In this paper, a data set of connector solder joint samples was made and the number of image samples was expanded to more than 3 times of the original by using data augmentation. Clustering generates anchor boxes and transfer learning with ResNet-101 were fused, so an improved faster region-based convolutional neural networks (Faster RCNN) algorithm was proposed. The experiment verified that the improved algorithm proposed in this paper had a great improvement in all aspects compared with the original algorithm. The average detection accuracy of this method can reach 94%, and the detection rate of some defects can even reach 100%, which can completely meet the industrial requirements.

Aiming at acquiring large deformation capability, powerful strength output, rapid response, and flexible locomotion, a novel three degrees-of-freedoms (DOFs) rolling parallel robot is proposed. This robot adopts the parallel mechanism, and its structure can guarantee the stiffness of the robot. The large capability of deformation can be obtained by taking advantage of the antiparallelogram mechanism with an enlarging mechanism of extension ratio. Hydraulic actuation is used for the telescopic input, which can increase the locomotion flexibility and the strength output of the robot. Rolling motion of the robot can be reached through planning and controlling the relations between the center of mass (CM) of the robot and the supporting region. The mechanical construction and configuration of the robot are described, the rolling gaits are planned, and the optimal locomotion law is given. Based on the law, the kinematic model of the robot is created. The kinematic model is validated by the given numerical example. The locomotion feasibility of two locomotion periods is analyzed. A set of experimental tests on the hydraulic system and the robotic system are performed. Results of four rolling experiments verify the reliability of the experimental system and the rapid response capability and also verify the validity and feasibility of the theoretical analysis and the rolling locomotion.