DIOR

Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large‐scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23463 images and 192472 instances, covering 20 object classes. The proposed DIOR dataset 1) is large‐scale on the object categories, on the object instance number, and on the total image number; 2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter‐ and intra‐class size variability across objects; 3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and 4) has high inter‐class similarity and intra‐class diversity. The proposed benchmark can help the researchers to develop and validate their data‐driven methods. Finally, we evaluate several state‐of‐the‐ art approaches on our DIOR dataset to establish a baseline for future research.

近年来，针对光学遥感图像中的目标检测任务，学界已投入大量研究工作并提出了诸多方法。然而，目前针对光学遥感图像目标检测领域的数据集与深度学习方法的综述研究仍存在不足。此外，现有多数数据集均存在一定缺陷：例如图像总量与目标类别数规模有限，且图像多样性与场景变化性不足。这些局限极大地制约了基于深度学习的目标检测方法的发展。本文中，我们针对计算机视觉与地球观测两个领域内，基于深度学习的遥感目标检测近期研究进展展开了全面综述。随后，我们构建了一个大规模、可公开获取的光学遥感图像目标检测基准数据集，并将其命名为DIOR。该数据集包含23463幅图像与192472个目标实例，涵盖20个目标类别。所提出的DIOR数据集具备如下特点：1）在目标类别数、目标实例数与总图像数三个维度均具备大规模特性；2）目标尺寸变化范围广泛，不仅体现在空间分辨率维度，还涵盖了不同目标间的类间与类内尺寸差异；3）图像采集场景差异显著，涵盖了不同的成像条件、天气状况、季节变化与图像质量水平；4）类别间相似性较高，同时类内多样性丰富。该基准数据集可助力研究人员开发并验证其数据驱动型方法。最后，我们在DIOR数据集上对多款当前前沿的目标检测方法进行了评估，为后续相关研究建立了基准性能基线。

搜集汇总

背景与挑战

背景概述

DIOR是一个大规模的光学遥感图像目标检测数据集，包含23,463张图像和192,472个实例，覆盖20个对象类别。该数据集具有规模大、对象尺寸变化范围广、图像多样性高以及类间相似性和类内多样性显著的特点，旨在支持深度学习方法的开发和验证，适用于遥感领域的计算机视觉研究。