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Small object detection in diverse application landscapes: a survey

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Abstract

The importance of object detection within computer vision, especially in the context of detecting small objects, has notably increased. This thorough survey extensively examines small object detection across various applications, consolidating and outlining the available methodologies. Traditional papers on small object detection have focused on specific domains. However, this survey paper incorporates insights from a multitude of domains, providing a comprehensive understanding of the versatility and applicability of small object detection techniques. This paper sheds light on the key challenges faced and delves into potential solutions to address the challenges, offering insights into viable solutions to enhance small object detection performance, setting it apart from existing literature. The strategies identified in our survey encompass a spectrum of approaches, categorized as transformer-based, CNN, and traditional methods. Also, this paper collates prevalent datasets relevant to small object detection, simplifying access to these resources. Further, it provides a succinct overview of diverse evaluation metrics used for performance assessment in this field, enhancing understanding of the effectiveness and proficiency of these methods. This survey paper not only consolidates established knowledge but also highlights innovative viewpoints, providing a comprehensive and enlightening compilation that contributes to the advancement of small object detection in the field of computer vision.

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Data Availibility Statement

This article does not entail data sharing, as it did not involve the creation or analysis of any datasets during the study.

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Acknowledgements

The authors would like to thank the Department of Science & Technology (DST), New Delhi, Govt. of India, for their support under the DST INSPIRE Fellowship Scheme and also extend sincere appreciation to the Department of Computer Science at the Islamic University of Science & Technology, India, for their invaluable assistance and provision of essential resources, which significantly contributed to the successful execution of this work.

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  1. Department of Computer Science, Islamic University of Science & Technology, Pulwama, India

    Iqra & Kaisar J. Giri

  2. Computer Vision & Biometrics Lab, Dept. of IT, Indian Institute of Information Technology, Allahabad, India

    Mohammed Javed

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  1. Iqra
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  3. Mohammed Javed
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Iqra - Conceptualization, Writing - Original Draft, Writing - Review & Editing. Kaisar Javeed Giri - Methodology, Writing - Review & Editing, Supervision. Mohammed Javed - Data curation, Validation, Supervision.

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Correspondence to Kaisar J. Giri.

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Iqra, Giri, K.J. & Javed, M. Small object detection in diverse application landscapes: a survey. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18866-w

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