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.
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.
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.