Using the satellite characterization information obtained by the space-based platform, the key parts of the satellite, such as solar panels, satellite payload, and propulsion systems, are segmented. The target object segmented from the point cloud data is significant to improve the accuracy of subsequent point cloud registration and attitude recognition. In this study, we introduced TSNet, which has the following characteristics. 1) The continuous recursive gate convolution module (gnConv) is introduced into the network, which can improve the accuracy of point cloud segmentation. 2) The weight channel for feature transfer is designed to avoid global information loss. The mIoU value of TSNet laser point cloud segmentation reached 88.12%, which was better than common point cloud segmentation algorithms, such as PointNet, PointNet++ and DGCNN. The proposed method can provide more accurate perception information for ground control personnel.
KEYWORDS: Target detection, Design, Process control, Portability, Detection and tracking algorithms, Control systems, Cameras, CCD cameras, Signal detection, Imaging systems
With the rise and development of microelectronics as well as the optoelectronics industry, micro photoelectric devices, like miniature cameras, pose a serious threat to the privacy and security of personal and public information. To be able to quickly and accurately detect micro photoelectric devices, this paper, which is based on the previous theoretical research of our group, designs and builds a portable detection system for micro photoelectric devices from three aspects: system design, hardware selection and software design. In the actual test, the detection time of our system meets the real-time requirements, and the detection accuracy reaches more than 90%.
Compressed sensing theory is a new sampling theory, which provides a method to recover the original signal from a small number of samples. For sparse signal and compressible signal, compressed sensing theory compresses the signal while sampling. It combines the sampling process and compression process. It breaks through the traditional Nyquist sampling law and saves a lot of storage, transmission, computing and other resources. This theory not only reduces the cost of storage and transmission of digital image and video acquisition, but also provides a new opportunity for the follow-up research of image processing and recognition, and promotes the combination of theory and engineering application. It includes three parts: sparse representation of target, design of measurement matrix and reconstruction of target. Reconstruction algorithm is a key step in the process of compression imaging, which determines the accuracy and speed of image reconstruction to a certain extent, so it is very important to select the appropriate image quality evaluation index. The image quality evaluation of existing reconstruction algorithms mainly focuses on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The advantages of these two algorithms lie in simple algorithm, fast inspection speed, which are suitable for evaluating the advantages and disadvantages of algorithms, but the disadvantage is that they can only be evaluated on the basis of known original images. In the actual imaging process of compressed sensing, it is impossible to obtain the original image, so we need to use an image quality evaluation method which is not based on the original image.
The existing space situation awareness optical imaging system is limited by the satellite platform and optical system, and it is difficult to realize large aperture observation and multi-dimensional optical characteristics data acquisition for rapid target detection. Aiming at the problem that the sparse aperture system is difficult to achieve clear imaging in all depth of field, and the image quality degradation caused by the defocusing and dislocation of the object point and phase closure, the refocusing imaging technology based on light field modulation is adopted to expand the synthetic aperture to full depth of field, and effectively compress the amount of data.
Images store a lot of information and are the window for human beings to understand things. A lot of research is devoted to analyzing and processing images, which is called image processing in a broad sense. Image processing includes image recognition, image restoration, image enhancement, image coding and so on. This paper mainly focuses on the field of image restoration. Image restoration, also known as image inverse problem, aims to restore high-quality original images from degraded or damaged observations. It also acts as a preprocessing step in many intermediate and advanced image processing tasks. Due to the limitations of sensors or environmental conditions, imaging systems usually have factors such as noise, optical or motion blur, resulting in image degradation and distortion. Aiming at the ill posed problem of image pixel missing and blur in the process of compression coding, this paper uses GMM model to solve the degraded image, so as to achieve the purpose of image restoration.
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