Abstract Object detection in images collected by Unmanned Aerial Vehicles (UAVs) constitutes a challenging task in computer vision, due to difficulties of learning a well-trained object detection model for handling instances in UAV images with arbitrary orientations, variation in different scales, irregular shapes, etc. In order to facilitate object detection research and extend its applications in natural scenarios by using UAVs, this paper presents a large-scale benchmark dataset, MOHR, aiming at performing multi-scale object detection in UAV images with high resolution. A total of 90,014 object instances with labels and bounding boxes were annotated. In order to build a baseline for object detection on the MOHR dataset, we performed an empirical study by evaluating six state-of-the-art deep learning-based object detection models trained on our proposed dataset. Experimental results show promising detection performance, but also demonstrate that the dataset is quite challenging for adopting natural image-based object detection models for UAV images.

中文翻译：

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高分辨率无人机图像中的多尺度目标检测：一项实证研究

摘要 无人机（UAV）收集的图像中的目标检测构成了计算机视觉中的一项具有挑战性的任务，因为难以学习训练有素的目标检测模型来处理具有任意方向、不同尺度变化、不规则形状的无人机图像中的实例。等。为了促进目标检测研究并通过使用无人机扩展其在自然场景中的应用，本文提出了一个大规模基准数据集 MOHR，旨在在无人机图像中进行高分辨率的多尺度目标检测。总共注释了 90,014 个带有标签和边界框的对象实例。为了在 MOHR 数据集上建立对象检测的基线，我们通过评估在我们提出的数据集上训练的六个最先进的基于深度学习的对象检测模型进行了一项实证研究。实验结果显示了有希望的检测性能，但也表明该数据集对于采用基于自然图像的无人机图像对象检测模型非常具有挑战性。