Deep transfer learning for military object recognition under small training set condition

Convolutional neural network is powerful for general object recognition. However, its excellent performance depends largely on huge training set. Facing task like military object recognition in which image samples for training are scarce, its performance will degrade sharply. To solve this problem, a deep transfer learning method is proposed in this paper. The main idea consists of two parts: transfer learning for prior knowledge embedding and mixed layer for better feature extraction. It has been proved that the ability of feature extraction learned in large dataset is helpful to related tasks and can be transferred to a new neural network. The transfer learning process is achieved by fixing the weights of some layers and then retraining the remained layers. The key problem for deep transfer learning is which part should be transferred and which part should be retrained to adapt the network to the new task. This problem is solved by extensive experiments, and it is found that retraining the last three layers and transferring prior to the other layers can reach the best performance. Besides, we used mixed layer scheme to make use of the current information. In each mixed layer, convolution filters in different scales are combined together, helping to adapt features in different scales. By employing these two methods, the proposed method exhibits a large improvement in military object recognition under small training set. Experiments demonstrate that our method can achieve a high recognition precision, superior to many other algorithms compared.