mlsanityData to 7 Oct 2026

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

arXiv 2207.12926 ↗First posted 26 Jul 2022cs.CV cs.LGcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 140 of its 313 references that are in the map. First posted on arXiv 26 Jul 2022.

Builds on

140 of 313 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 4; 2026: 5.

14 papers.

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  1. 2023Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Artcited by 15
  2. 20221st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Resultscited by 14
  3. 2023MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Resultscited by 7
  4. 2024Faster Image2Video Generation: A Closer Look at CLIP Image Embedding's Impact on Spatio-Temporal Cross-Attentionscited by 4
  5. 2026HART: High-Resolution Annotation-Free Reasoning Technique through a Closed-loop Frameworkcited by 3
  6. 2025MVQA-68K: A Multi-dimensional and Causally-annotated Dataset with Quality Interpretability for Video Assessmentcited by 2
  7. 2023Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisonscited by 1
  8. 2025MVTD: A Benchmark Dataset for Maritime Visual Object Trackingcited by 1
  9. 2026WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objectscited by 1
  10. 2026WD-FQDet: Multispectral Detection Transformer via Wavelet Decomposition and Frequency-aware Query Learningcited by 1
  11. 2025An Empirical Study of Methods for Small Object Detection from Satellite Imagerycited by 0
  12. 2025LSFDNet: A Single-Stage Fusion and Detection Network for Ships Using SWIR and LWIRcited by 0
2 more
  1. 2026GSO-Net: Visual State Machines for Hazardous Freight Transfer Compliance at Petrochemical Logistics Nodescited by 0
  2. 2026C2FXNet: Coarse-to-Fine Scene Expert for Unified Object Detection across Adverse Weathercited by 0