Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement

With the advent of drones, new potential applications have emerged for the unconstrained analysis of images and videos from aerial view cameras. Despite the tremendous success of the generic object detection methods developed using ground-based photos, a considerable performance drop is observed when these same methods are directly applied to images captured by Unmanned Aerial Vehicles (UAVs). Usually, most of the work goes into improving the performance of the detector in aspects such as design loss, training sample selection, feature enhancement, and so forth. This paper proposes a detection framework based on an anchor-free detector with several modules, including a sample balance strategies module and super-resolved generated feature module, to improve performance. We proposed the sample balance strategies module to optimize the imbalance among training samples, especially the imbalance between positive and negative, and easy and hard samples. Due to the high frequencies and noisy representation of the small objects in images captured by drones, the detection task is extraordinarily challenging. However, when compared with other algorithms of this kind, our method achieves better results. We also propose a super-resolved generated GAN (Generative Adversarial Network) module with center-ness weights to effectively enhance the local feature map. Finally, we demonstrate our method’s effectiveness with the proposed modules by carrying out a state-of-the-art performance on Visdrone2020 benchmarks.