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

• URL: http://arxiv.org/abs/2207.12926v1
• Date: Tue, 26 Jul 2022 14:28:38 GMT
• Title: A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance
• Authors: Aref Miri Rekavandi, Lian Xu, Farid Boussaid, Abd-Krim Seghouane, Stephen Hoefs and Mohammed Bennamoun
• Abstract summary: Small object detection in optical images and videos is a challenging problem. Even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research.
• Score: 38.88995659233979
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future [119.88454942558485]
  Underwater object detection (UOD) aims to identify and localise objects in underwater images or videos. In recent years, artificial intelligence (AI) based methods, especially deep learning methods, have shown promising performance in UOD.
  arXiv  Detail & Related papers  (2024-10-08T00:25:33Z)
• Beyond Few-shot Object Detection: A Detailed Survey [25.465534270637523]
  Researchers have introduced few-shot object detection (FSOD) approaches that merge few-shot learning and object detection principles. These approaches play a vital role in reducing the reliance on extensive labeled datasets. This survey paper aims to provide a comprehensive understanding of the above-mentioned few-shot settings and explore the methodologies for each FSOD task.
  arXiv  Detail & Related papers  (2024-08-26T13:09:23Z)
• Few-Shot Object Detection: Research Advances and Challenges [15.916463121997843]
  Few-shot object detection (FSOD) combines few-shot learning and object detection techniques to rapidly adapt to novel objects with limited annotated samples. This paper presents a comprehensive survey to review the significant advancements in the field of FSOD in recent years.
  arXiv  Detail & Related papers  (2024-04-07T03:37:29Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Spatial Reasoning for Few-Shot Object Detection [21.3564383157159]
  We propose a spatial reasoning framework that detects novel objects with only a few training examples in a context. We employ a graph convolutional network as the RoIs and their relatedness are defined as nodes and edges, respectively. We demonstrate that the proposed method significantly outperforms the state-of-the-art methods and verify its efficacy through extensive ablation studies.
  arXiv  Detail & Related papers  (2022-11-02T12:38:08Z)
• Few-Shot Object Detection: A Survey [4.266990593059534]
  Few-shot object detection aims to learn from few object instances of new categories in the target domain. We categorize approaches according to their training scheme and architectural layout. We introduce commonly used datasets and their evaluation protocols and analyze reported benchmark results.
  arXiv  Detail & Related papers  (2021-12-22T07:08:53Z)
• Contrastive Object Detection Using Knowledge Graph Embeddings [72.17159795485915]
  We compare the error statistics of the class embeddings learned from a one-hot approach with semantically structured embeddings from natural language processing or knowledge graphs. We propose a knowledge-embedded design for keypoint-based and transformer-based object detection architectures.
  arXiv  Detail & Related papers  (2021-12-21T17:10:21Z)
• Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection [86.86602297364826]
  We propose a discoveryand-selection approach fused with multiple instance learning (DS-MIL) Our proposed DS-MIL approach can consistently improve the baselines, reporting state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-10-18T07:06:57Z)
• Learning Open-World Object Proposals without Learning to Classify [110.30191531975804]
  We propose a classification-free Object Localization Network (OLN) which estimates the objectness of each region purely by how well the location and shape of a region overlaps with any ground-truth object. This simple strategy learns generalizable objectness and outperforms existing proposals on cross-category generalization.
  arXiv  Detail & Related papers  (2021-08-15T14:36:02Z)
• Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview [8.442460766094674]
  Object pose detection and tracking has attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Deep learning is the most promising one that has shown better performance than others. This paper presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route.
  arXiv  Detail & Related papers  (2021-05-29T12:59:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.