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Miniscule Object Detection in Aerial Images Using YOLOR: A Review

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Proceedings of International Conference on Communication and Computational Technologies

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Object detection has made immense improvements in natural images during the last decade but not so much in aerial images. Detection of miniature objects in aerial images remains challenging as they contain only a few pixels and extremely large input sizes. Moreover, tiny objects are easily fooled by the backstory and increase the difficulty of accurate detection. Many algorithms are used for object detection purposes, and YOLOR is one of them. YOLOR “You Only Learn One Representation” is a one-stage detector. It is specially made for object detection, whereas other algorithms include object classification or analysis. In CNN, only, one task is carried out at a time, whereas YOLOR is a unified model useful for multitasking purposes. In this paper, we discussed tiny object detection in aerial images using YOLOR. Based on our research, we found that the AI-TOD dataset contains object instances in eight categories, with 86% of the objects being smaller than 16 pixels in size. The AI-TOD can be used to assess the performance of a variety of small objects. The mean size of objects is approximately 12.8 pixels, which is considerably smaller than the other datasets.

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Author information

Authors and Affiliations

  1. Modern Education Society’s College of Engineering, Pune, 411001, India

    Neha Pawar, Ashwini Waghmare, Anuja Pratap, Anjalee Thorat & Nuzhat Faiz Shaikh

  2. Centre for Development of Advanced Computing, Pune, India

    Kedar N. Ghogale & Shankar Naik Rathod Karamtoth

Authors
  1. Neha Pawar
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  2. Ashwini Waghmare
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  3. Anuja Pratap
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  4. Anjalee Thorat
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  5. Kedar N. Ghogale
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  6. Shankar Naik Rathod Karamtoth
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  7. Nuzhat Faiz Shaikh
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Corresponding author

Correspondence to Neha Pawar .

Editor information

Editors and Affiliations

  1. Christ (Deemed to be University), Bengaluru, Karnataka, India

    Sandeep Kumar

  2. Rajasthan Institute of Engineering and Technology, Jaipur, Rajasthan, India

    Saroj Hiranwal

  3. Rajasthan Technical University, Kota, Rajasthan, India

    S. D. Purohit

  4. School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia

    Mukesh Prasad

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Pawar, N. et al. (2023). Miniscule Object Detection in Aerial Images Using YOLOR: A Review. In: Kumar, S., Hiranwal, S., Purohit, S.D., Prasad, M. (eds) Proceedings of International Conference on Communication and Computational Technologies . Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-3951-8_52

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