In order to study the effect of flight altitude on the radiation characteristics of engine lateral jet, based on the simulation results of three-dimensional flow field, radiation transfer equation and molecular spectral line parameter database, we applied the apparent light line of sight method to solve the lateral jet radiation transfer equation and established a procedure to calculate the infrared radiation characteristics of the lateral jet of the attitude control engine; Correct the spectral line intensity of gases at high temperature and pressure. Using the spectral band model to calculate the spectrum absorption coefficients. The infrared radiation characteristics of the lateral jet of the attitude control engine at different flight altitudes are studied, and the distribution of the infrared radiation brightness of the lateral jet in different bands is obtained. The lateral jet spectral irradiance of the attitude control engine decreases with the increase of flight altitude in the low altitude environment, and increases with the increase of flight altitude in the high altitude environment. The results show that the program can simulate the infrared radiation characteristics of the lateral jet of the attitude control engine well and is widely applicable; Different flight altitudes affect the infrared radiation characteristics of the lateral jet of the engine to a certain extent and the flight altitudes at low and high altitudes have different effects on the radiation characteristics of the lateral jet of the attitude control engine.
High altitude balloon is a kind of aerostat working in the near space. Based on the basic radiation theory of physical optics, this paper takes the high altitude balloon as the research object. On the basis of summarizing and analyzing the research status of the high altitude balloon. Through the study of the thermodynamic model, the infrared radiation characteristics of the high altitude balloon are simulated. Through the study of the characteristics of the environmental factors of the high altitude balloon in the adjacent space, the infrared radiation of the environment of the high altitude balloon is simulated. Through the study of common atmospheric models, the atmospheric transmittance in the process of infrared radiation transmission is simulated. Finally, based on the linear quantization method, the radiation value is transformed into gray value, and then the infrared simulation image of high altitude balloon is obtained. The research and simulation results of this paper have certain reference value for the infrared detection and recognition of the high altitude balloon target.
Facial component and landmark detection have many applications in many facial analysis tasks. In this paper, a semisupervised method for this task is proposed to detect facial components and landmarks. Different from other facial detectors algorithms, our model without extra input solve the occlusion problem by detecting the visible facial components. Firstly, we propose a data augmentation method based on the Deep Convolutional Generative Adversarial Network to generate a large amount of semi-supervised training data. Then, a semi-supervised learning model based on Region-based CNN is responsible for multi-task facial component and landmark detection by training on the generated semi-supervised training data. During training, facial component regions and landmarks are used as supervised training data, while unsupervised training data only contains component bounding box. Experimental results illustrate that the proposed model can handle multi-task facial detection, and outperforms the state-of-the-art algorithms.
Image segmentation has always been a key research issue in the field of computer vision. Image segmentation networks that use deep learning methods require a large number of finely labeled samples, which is difficult to obtain. In this paper, we combine the focal loss function with the fully convolutional networks to improve network performance. And we collected and built a dataset contents 1500 samples with complex background. We trained the improved network with the dataset to achieve 81.55% in mean average precision and 76.13% in mean intersection over union.
Collecting aerial data from physical world is usually time-consuming. Image simulation is a significant data source for various ground target detection systems. Unfortunately, the reality gap between simulated and real data makes the model trained on simulated image not workable on real image. A translation method is proposed for tackling the simulation-toreality problem in this paper. First, image simulation system is employed for data preparation. Then, the simulated data is converted into a more similar one to the real image. The segmentation map is the bridge between simulated and real data. At last, the target detection model is used as the utility evaluation method for the simulated data. The simulated and synthesized data is used to train a vehicle detection model. Experiments show that results trained by synthesized data are really close to the real results. The proposed translation method can assist real image for target detection task, which is an effective data augmentation method for aerial data.
When the real infrared image is insufficient, the simulation infrared image is an important data supplement to the real infrared image. However, the authenticity of simulated infrared image often does not meet the requirements of real images. So improving the authenticity of simulated infrared image plays an important role in related fields. In order to achieve this goal, a method based on deep learning is proposed in this paper. Unlike traditional methods of using manual modification by experience, the proposed method can convert non-realistic simulation infrared image input into a realistic one with similar scene structure. First, we generate a large number of simulation infrared images through the simulation system. Then, we propose an optimization method to improve the authenticity of simulated infrared images. Finally, we designed a comparison experiment between the original simulation infrared image and the optimized simulation infrared image, and finally verify the effectiveness.
KEYWORDS: Detection and tracking algorithms, RGB color model, Video, Target acquisition, Image processing, Automatic tracking, Image transmission, Video processing, Field programmable gate arrays, Analog electronics
This paper designs a moving target tracking system. Firstly, on the aspect of hardware, a moving target tracking platform is designed, which consists of visible CCD camera, servo PTZ, FPGA control card and PC. The platform can achieve target image acquisition, image transmission, PTZ control and algorithm processing. Secondly, on the aspect of algorithm, aiming at the deficiency of the Mean Shift (MS) algorithm in poor anti-background interference ability, a target modeling method is proposed. The method fuses HSV color feature and edge orientation feature for Mean-Shift iteration. Then the improved algorithm is tested on the DAPRA Egtest01 test sets. Experiment results show that the improved algorithm is good at tracking target whose color is similar to the background color. Finally, the improved algorithm is implemented on the hardware platform. The real-time automatic tracking experiment shows that the system can obtain a satisfied tracking target result.
Infrared (IR) image simulation is an important data source for various target recognition systems. However, whether simulated IR images could be used as training data for classifiers depends on the features of fidelity and authenticity of simulated IR images. For evaluation of IR image features, a deep-representation-based algorithm is proposed. Being different from conventional methods, which usually adopt a priori knowledge or manually designed feature, the proposed method can extract essential features and quantitatively evaluate the utility of simulated IR images. First, for data preparation, we employ our IR image simulation system to generate large amounts of IR images. Then, we present the evaluation model of simulated IR image, for which an end-to-end IR feature extraction and target detection model based on deep convolutional neural network is designed. At last, the experiments illustrate that our proposed method outperforms other verification algorithms in evaluating simulated IR images. Cross-validation, variable proportion mixed data validation, and simulation process contrast experiments are carried out to evaluate the utility and objectivity of the images generated by our simulation system. The optimum mixing ratio between simulated and real data is 0.2≤γ≤0.3, which is an effective data augmentation method for real IR images.
Tank, as a vital ground weapon, plays an irreplaceable role in the war. The article did the research of infrared image of the tank. Firstly, the 3D model of tank was established. And then the infrared radiation model of the target was constructed by analysing the infrared characteristics of the tank’s different parts.. Finally the infrared radiation value of the tank under different states was calculated and the simulation of infrared characteristics of the tank under different states was done, which will provide reference for the research on infrared characteristics of the army's battlefield target.
Template matching algorithm is one of the important image-based Automatic Target Recognition methods. Traditional normalized cross correlation (NCC) algorithm used in infrared image matching has a strong antinoise performance but low computing speed. Meanwhile, although sequential similarity detection algorithm (SSDA) performs a shorter time than NCC, it has lower accuracy. In order to solve the low target recognition rate and slow speed of infrared image recognition problems, a new matching algorithm based on infrared image is presented, which integrates the advantages of two methods. The fusion algorithm improves the matching speed and reduces the probability of matching error. The experimental results confirm that the proposed approach has higher efficiency and accuracy in infrared image matching than original algorithms. Comparing with NCC and SSDA, it shortens large recognition time and enhances the right matching ratio respectively. In addition, the improved algorithm is real-time and robust against noise. It is significant to the research and development of automatic target recognition technology for different kinds of real-time detection system.
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