  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.
