An empirical study of multi-scale object detection in high resolution UAV images
An empirical study of multi-scale object detection in high resolution UAV images
复制标题
高分辨率无人机图像多尺度目标检测的实验研究
DOI：
10.1016/j.neucom.2020.08.074
复制
发表时间：
2021-01-15
期刊：
影响因子：
6
通讯作者：
Ji, Yuzhu
中科院分区：
文献类型：
--
作者：
Zhang, Haijun;Sun, Mingshan;Ji, Yuzhu
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. (c) 2020 Elsevier B.V. All rights reserved.
