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.