Detection with fast feature pyramids and lightweight convolutional neural network: a practical aircraft detector for optical remote images

Abstract

Aircraft detection in optical remote sensing images has important practical value and attracts great attention in recent years. Due to some clustered backgrounds and various deformations like variability in viewpoints, scales, and directions, it is still a big challenge. In recent years, deep learning, especially convolutional neural networks (CNNs), has achieved remarkable success not only in natural image object detection tasks but also in remote sensing image object detection tasks. However, due to the limitations of the actual application deployment environment only limited processing units and storage space can be used, and most deep learning-based aircraft detection algorithms cannot work well. To this end, a unique aircraft detection framework in optical remote sensing images is proposed, which is based on fast feature pyramids and a specialized lightweight CNN. The algorithm can be summarized into two steps. In the first step, a highly reliable region proposal generation algorithm is designed to predict region proposals. In this phase, a prior square sliding window method and an efficient linear search algorithm are proposed. In the next step, a lightweight CNN is designed to achieve fast and accurate aircraft classification. The proposed model only requires about 237 KB of parameter storage and can infer in a poor central processing unit platform. Comprehensive experiments on three publicly optical remote sensing aircraft datasets demonstrate the superiority and effectiveness of the proposed method. It is also shown that the proposed method is light and fast in comparison with some state-of-the-art methods.