Night-Time Aerial Image Vehicle Recognition Technology Based on Transfer Learning and Image Enhancement

In order to identify vehicles in night-time aerial images effectively, this paper proposed an image processing technique based on two-time transfer learning and the Retinex algorithm. It only used a small-scale data set to train the network and then employed a deep learning algorithm based on Faster R-CNN to achieve quick detection of vehicles. Firstly, a transfer learning process was applied between the large-scale ImageNet data set and the mid-scale Chinese Academy of Sciences daytime aerial data set, and then a second transfer learning algorithm was utilized from the day-time mid-scale data set to the night-time small-scale data set. At the same time, the Retinex iterative algorithm was used to process the night-time pictures to enhance their similarity with the day-time pictures, so that the second transfer learning can be effectively performed. The experimental results show that this method can train an effective recognition network on deep learning platforms with small-scale data sets, and its detection performance is superior to the traditional machine learning methods. It also has certain application values in military reconnaissance and traffic control field. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.