# An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image
## 1 Introduction  
At present, computer vision (CV) and natural language processing (NLP) and speech recognition are listed as three hotspot directions of artificial intelligence (AI). Computer vision can be used in many fields, such as detection of asteroids [1], meteors [2] and atmospheric events [3], self-driving [4], biomedical image detection [5], automated defect inspection [6], satellite image analysis [7], criminal analysis [8]. One of the classic applications is aircraft object detection. Object detection algorithm is used to identify which real-world objects [9] are and where the objects are in the image. For example, Figure 1 is an image in the Pascal VOC dataset [10], object detection algorithm needs to identify the bus and person in the image and locate them. Object detection algorithms can be used in a great number of fields, such as face detection, and shape detection [9]. Therefore, object detection has received great attention in the field of remote sensing, and it also faces many challenges [11]. Traditional object detection has gone through several important phases. [PERSON] and [PERSON] use Harr-like features [12] to train strong classifiers in AdaBoost [13; 14], and then implement face detection by using sliding window search strategy. [PERSON] and [PERSON] propose the local Histogram of Oriented Gradient (HOG) as the image feature, and support vector machine (SVM) as the classifier to detect pedestrians [15]. [PERSON] et al. [16; 17] propose a multiscale Defomable Parts Model (DPM) method [18] based on the idea that most objects can be regarded as rigid bodies. [PERSON] et al. adopt the integration idea in machine learning, and train an SVM classifier for each positive sample by using HOG features [19]. [PERSON] et al. design the histogram of sparse code (HSC) based on sparse representation theory [20]. With the swift improvement of computer hardware performance, convolutional neural networks (CNN) [21] have once again attracted the attention of researchers. In the Large Scale Visual Recognition Challenge (ILSVRC) 2012 [22], [PERSON] and [PERSON] used the AlexNet [23] to achieve a huge leap in detection performance on the ImageNet dataset [24]. Therefore, CNN has received extensive attention in the field of computer vision. Since the advent of AlexNet, a large number of CNN-based object detection algorithms have appeared. In 2014, [PERSON] et al. applied CNN to object detection and proposed R-CNN algorithm [25]. Firstly, a selective search algorithm was used to generate a series of candidate regions. Then CNN was used to extract these features in these regions. Finally, the SVM classifier was trained for classification. In 2015, [PERSON] et al. proposed SPP-net [26], which solved the problem of input image size limitation by using spatial pyramid pooling, and mapped the candidate regions in the original image directly to the feature map. This makes SPP-net faster than R-CNN by dozens of times. In 2015, [PERSON] proposed Fast R-CNN [27], which uses two parallel different fully connected layers to perform classification and positioning tasks separately. [PERSON] et al. proposed the Faster R-CNN [28] algorithm, which uses neural networks to extract candidate regions, i.e., RPN networks. Since the extraction of the candidate region is time-consuming and complicated, some researchers directly find the position and category information of the object of interest on the input image. The most representative algorithms are YOLO [29] and Single Shot Multi-Box Detector (SSD) [30]. In 2018, [PERSON] proposed a generic 3D detection network, i.e., VoxelNet [31]. It can perform feature extraction and bounding box prediction into a single stage, end-to-end trainable deep network. Although deep learning has achieved great success in object detection, the detection performance will be significantly reduced in the case of insufficient datasets. Generally, spaceborne optical remote sensing images are difficult to obtain and costly.