Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.
翻译:光学图像和视频中的小型物体探测(SOD)是一个具有挑战性的问题,即使是最先进的通用物体探测方法也未能准确定位和识别此类物体。一般情况下,小型物体出现在现实世界中,原因是摄像物体距离太远。由于小物体在输入图像中只占据一个小区域(例如不到10%),从如此小区域提取的信息并不总是足以支持决策的丰富程度。在深层次学习和计算机视野的界面上工作的研究人员正在开发多学科战略,以提高SPOD深层次学习方法的性能。在本文件中,我们全面审查了2017年至2022年期间发表的160多份研究论文,以调查这一日益扩大的主题。本文概述了现有文献,并提供了一种分类,以说明当前研究的广泛情况。我们调查如何改进小型物体探测在海洋环境中的性能,因为那里的性能越来越重要。通过建立通用和海洋SOD研究之间的联系,确定了今后的方向。此外,还讨论了用于特殊用途的深度研究应用方法的流行数据集。此外,还讨论了关于某些通用和海上应用的SOD的通用数据评估方法。