# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 1 Introduction  
Object detection has been widely studied for decades [1]. The most famous detectors, such as those used for surveillance, mainly focus on the object of interest in images captured by ground-based cameras [2]. However, with the advantages of low cost, high flexibility, simple operation, and a small size, camera-equipped drones have been rapidly developed and deployed to replace satellites and cameras for a wide range of applications, such as in agriculture, aerial photography, delivery, surveillance, as well as in other fields [3]. Object detection is therefore one of the key technologies that will improve the perception capability of drones, and in addition, it is the basis for other intelligent algorithms, such as segmentation [4], object tracking [5], crowd estimation [6], etc. Despite the high demand for this technology, the drone-based detection algorithm still poses more challenges than the traditional ground-based detection algorithm. Progress has been slow in the research on object detection for drones, and this has gradually become one of the bottlenecks restricting the development of drones. The level of accuracy and real-time object detection will determine whether the drones' mission will end with the destruction of the aircraft or its safekeeping. Limited by electric power, range, and environment, the drone-based object detection algorithm brings certain challenges:  
1. The instability of fast-moving UAVs means that aerial images are often blurred and noisy [7]. In addition, less feature information is extract-able from these movingtargets, the drone may repeatedly detect the same object, and it may falsely detect a target;
2. The objects in need of detection are generally small in the images [8]. This means that when the UAV takes photos from high up, small targets are easily missed;
3. The UAV's continuous movement and the changes in the external environment (such as light, clouds, fog, rain, etc.) lead to drastic changes in the target's features within the image, and thus increase the difficulty of subsequent feature extraction [9];
4. The drone-based object detection algorithm needs to quickly and accurately detect moving targets [10], so the algorithm must meet real-time computing requirements.  
Since the target usually appears small in drone images, the object's features are often unclear and can easily be confused with the features of other objects. In addition, having excessive background in the image can lead to having too many negative samples in the training process, which affects detection accuracy. Motivated by these observations, this paper aims to improve the efficiency and accuracy of a drone object detection system based on the challenges mentioned above. We offer to study an object detection model based on the idea of an anchor-free framework that can reduce the amount of computation of IoU (Intersection over Union) [11]. In order to adapt the positive and negative samples, we propose new sample selection strategies. In addition, the weight-Generative Adversarial Network (GAN) sub-network is proposed to enhance the features locally. Following this, experiments carried out on Visdrone datasets [12] are used to demonstrate our method's advantage over state-of-the-art detection methods.