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.
Proc. SPIE. 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018)
KEYWORDS: Detection and tracking algorithms, Image processing, Video, Field programmable gate arrays, Image transmission, Video processing, Analog electronics, Target acquisition, RGB color model, Automatic tracking
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.