Aircraft detection in remote sensing imagery with lightweight feature pyramid network

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Object detection is an important part of remote sensing image processing and analysis. Traditional object detection methods in remote sensing imagery encounter with tough challenges when detecting small objects such as aircrafts and automobiles, due to complex background clutter, small target size, variation of visual angle, etc. We propose a targets detection network to detect the aircrafts in large-format remote sensing imagery based on deep convolutional neural network. Our method utilizes the Feature Pyramid Network (FPN [1]) to extract and inosculate multi-scale convolutional features to model the characteristics of targets and background. Moreover, in order to reduce the computational complexity of convolutional neural network, we utilize MobileNet [2] as backbone network and propose a computational efficient region proposal structure. We collect and manually annotate a dataset for aircrafts detection in remote sensing imagery in order to evaluate the proposed method. We achieve an average precision (AP) of 0.91 on the dataset, which is superior to other state-of-the-art methods, while our model is still faster and more compact than other models.