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.
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.
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.