## 1 Introduction  
Object detection is one of the fundamental tasks in computer vision and has attached wide attention due to the success of deep learning [1]. With the development of sensor and aerospace technology, large volume and high-resolution remote sensing images have accumulated. Object detection in remote sensing images has become a research hotspot [2; 3; 4]. Currently, aircraft are a common means for transportation and warfare. Therefore, aircraft detection in remote sensing images plays a great significance in civilian air traffic management and military intelligence [5], especially detecting accurate aircraft position and size information, and has crucial practical significance and military value for such as enemy's operational readiness analysis and regional situation assessment. Aircraft detection consists of two parts: locating object regions and classifying object in the candidate regions. In order to meet the requirements of rapid and accurate aircraft detection, it is necessary to get invariant features and train a classifier with a strong generalized capability. Extensive research has focused on feature design and feature expression to get good aircraft detection results. It usually constructs rotation-invariant and scale-invariant features based on object shape, texture, and geometric features and collaborates with the general classifiers, e.g., SVM, Bayes, KNN, and other classifiers. Most methods realize object detection by extracting excellent features, such as Harris corner feature [6; 7], Histogram of oriented gradient (HOG) [8], scale-invariant feature transform (SIFT) [9; 10], and Fourier descriptor [11; 12]. These methods have achieved good performance in the field of object detection, but the aircraft scales and shapes in the optical remote sensing images are complex and diverse, and the manual features are difficult to adapt to the diverse remote sensing images. The methods based on manual-designed features are laborious and lack generalization ability, which leads to the disadvantage that the accuracy cannot meet the requirements of real applications. Recently, with the great success of deep learning, much progress has been made in object detection and recognition. Since 2012, [PERSON] proposed a convolutional neural network (CNN) model called AlexNet, which won the championship of 2012 ImageNet Challenge [13]; many CNN-based deep learning models have mushroomed. The popular detection models can be divided into two categories: two-stage and one-stage object detectors. The two-stage methods, such as R-CNN, Fast R-CNN, and Faster R-CNN [14; 15; 16], mainly consist of region proposals generation followed with feature extraction from these regions, and then classifiers and regressors used for classification and regression in the second stage. The one-stage method has obvious advantages in efficiency, especially for the large scale of remote sensing images [17]. OverFeat [18] is one of the first one-stage detectors based on CNN. It performs object detection in a multiscale sliding window fusion via single forward propagation through the CNN. Compared with region-based methods, [PERSON] et al. [19] proposed YOLO, a unified detector, casting object detection as a regression problem from image pixels to spatially separated bounding boxes and associated class probabilities. To preserve real-time speed without sacrificing too much detection accuracy, [PERSON] et al. [20] proposed single shot multibox detector (SSD). The work solves the class imbalance problem by proposing RetinaNet with Focal loss [21] and further improves the accuracy of one-stage detector. Following their works, [PERSON] et al. [22] used the saliency detection algorithm to reduce the number of proposal boxes. They obtained the object position information using the saliency algorithm based on the background priori, and then used a deep CNN to determine the category and fine-tune bounding boxes of the objects. [PERSON] et al.