With the improvement of computer computing power, the object detection algorithms based on deep neural network has ushered in vigorous development, and has been widely used in industry, agriculture, medicine, military and other fields. One-stage object detection algorithms shows the superiority in real-time detection compared to other object detection algorithms such as two-stage object detectors or ViT-based detectors. At the same time, more and more anchor-free detectors show the advanced nature of anchor-free algorithms compared to anchor-based detectors. In this paper, we review the one-stage anchor-free real-time object detection algorithms in recent years, and analyze the application scenarios and optimization strategies of future object detection algorithms. Firstly, the principle and advantages of anchor-free object detection algorithms and one-stage object detection algorithm are introduced. Secondly, the network structure and innovation of anchor-free object detection algorithms in recent years are summarized. Finally, the possible development direction and trend of one-stage anchor-free real-time object detection algorithms in the future are proposed.
In this paper, we propose a method to calculate the position of interest objects in the image by matching the visual image with the three-dimensional model. Through the sensor and 3D model from the height accuracy of geographic information, to eliminate the error and improve the positioning accuracy. In this method, two visible light sensors are used for positioning, SIFT algorithm is used for image matching, and feature points are obtained. The obtained feature points are mapped to the known 3D model. Using the mapped geographic information, 3D model coordinate information and geographic information, the corresponding relationship between 3D model coordinates and original image coordinates is obtained. Finally, the precise positioning information is obtained through the pixel coordinates of the target in the image. Compared with the existing algorithms, the proposed method can achieve high-precision real-time geographic positioning, and the positioning calculation process is simple, which can effectively improve the positioning efficiency.
The core optical component of a composite IRST system is a scanning space mirror component, which is the mirror which shape of optical reflector possessing large flakiness ratio, which always effected by the wide-range temperature and harsh environment (especially in – 45°C)in optical system. To suppress the deformation, a flexible support structure for the reflector is designed in this paper. Based on the flexible support principle and thermodynamics theory, a new flexible support structure is designed for small-light-strip space reflector. A flexible support structure was proposed to keep the surface figure accuracy under temperature change load and 0.06mm installation error cases. By adopting finite element analysis software, the mirror component was analyzed. And some experiments are carried on the optical reflector system in-45°C and 0.06mm forced linear displacement. The result shows that the accuracy of reflector surface remains 0.2λPV(λ=632.8nm, peak-to-valley. These results demonstrate that the novel flexible supporting structure design satisfies the application environment and space requirements.
(Visual Simultaneous Location and Mapping) VSLAM is the key technology of computer vision and equipment intelligence. Firstly, this paper introduces the main algorithms and historical evolution of VSLAM, and summarizes the existing algorithms from the front-end and back-end classification, advantages and disadvantages. Subsequently, new research directions emerged in recent years are introduced, focusing on the combination of depth, semantics and multi-robots. Finally, the existing problems are summarized, and the future direction of development is prospected.
KEYWORDS: Visualization, Optoelectronics, 3D modeling, 3D image processing, Data modeling, Data fusion, Associative arrays, Visual process modeling, Data communications, Image processing
Introduces the image and spatial data fusion in photoelectric detection application. Focus on the optimization of 3D scene reconstruction. In airborne imaging applications, aiming at the problems of massive terrain data, this paper proposed a dynamic data scheduling strategy which is based on state-tree from simplification, and present a terrain data dynamic schedule framework from render optimization. For the suggested optimized procedure and framwork, give a experiment and couclusion based on programmimg, it prove that the suggested dynamic schedule strategy in this paper could fastly construct three-dimensional scene in flight simulation, could speed up the three-dimensional visulization, it could meet the practical requiremnet of engineering in flight simulation.
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