深度学习的遥感图像旋转目标检测综述

doi:10.3778/j.issn.1673-9418.2308031

摘要/Abstract

摘要： 遥感图像中目标具有方向任意性和排列紧密性的特点，在检测任务中使用倾斜边界框可以更加精确定位和分离目标。目前遥感图像旋转目标检测已经广泛应用于民用和军事国防领域，具有重要的研究意义和应用价值，已逐步成为研究热点。鉴于此，对遥感图像中旋转目标检测方法进行了系统性总结。首先，介绍了三种常用的倾斜边界框的表示形式。其次，重点阐述全监督学习下的特征错位、边界不连续、度量值与损失不一致性、旋转目标定位四个挑战。然后，根据不同的动机和改进策略，详细阐述了每种方法的核心思想及其优缺点，归纳出旋转目标检测方法框架。接着，列举了旋转目标检测在遥感领域常用数据集，给出了经典方法在不同数据集上的实验结果，并对不同方法的性能进行了评估。最后，结合深度学习应用于遥感图像旋转目标检测任务中存在的挑战，对该方向的未来发展趋势进行了展望。

关键词: 旋转目标检测, 倾斜边界框, 遥感图像, 深度学习

Abstract: The objects in remote sensing images have the characteristics of arbitrary direction and dense arrangement, and thus objects can be located and separated more precisely by using inclined bounding boxes in object detection task. Nowadays, oriented object detection in remote sensing images has been widely applied in both civil and military defense fields, which shows great significance in the research and application, and it has gradually become a research hotspot. This paper provides a systematic summary of oriented object detection methods in remote sensing images. Firstly, three widely-used representations of inclined bounding boxes are summarized. Then, the main challenges faced in supervised learning are elaborated from four aspects: feature misalignment, boundary discontinuity, inconsistency between metric and loss and oriented object location. Next, according to the motivations and improved strategies of different methods, the main ideas and advantages and disadvantages of each algorithm are analyzed in detail, and the overall framework of oriented object detection in remote sensing images is summarized. Furthermore, the commonly used oriented object detection datasets in remote sensing field are introduced. Experimental results of classical methods on different datasets are given, and the performance of different methods is evaluated. Finally, according to the challenges of deep learning applied to oriented object detection in remote sensing images tasks, the future research trend in this direction is prospected.

Key words: oriented object detection, inclined bounding box, remote sensing images, deep learning

蓝鑫, 吴淞, 伏博毅, 秦小林. 深度学习的遥感图像旋转目标检测综述[J]. 计算机科学与探索, 2024, 18(4): 861-877.

LAN Xin, WU Song, FU Boyi, QIN Xiaolin. Survey on Deep Learning in Oriented Object Detection in Remote Sensing Images[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(4): 861-877.

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