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A Review of Deep Learning-Based Object Detection Current and Future Perspectives

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Proceedings of Third International Conference on Sustainable Expert Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 587))

Abstract

Object detection is a method to detect and localize the objects present in images and videos and stands as one of the challenging fields of computer vision. Object detection plays a crucial role in multiple real-time applications like video surveillance, autonomous driving, medical image processing, etc. Object detection key challenges such as detecting small objects and addressing class imbalance are addressed with various deep learning detection models. This review article classifies the object detection models into three main categories—Two-stage, One-stage and Transformer based detectors discussing the recent advanced developments in object detection listing some of the most important works in each category. Benchmark datasets used for object detection task with different metrics used for evaluating the performance of object detectors are listed. Performance comparison among various object detectors is plotted showing how advanced detectors achieve better accuracy compared to the existing example detectors.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, VIT-AP University, Amaravathi, Andhra Pradesh, India

    Museboyina Sirisha & S. V. Sudha

Authors
  1. Museboyina Sirisha
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  2. S. V. Sudha
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Corresponding author

Correspondence to Museboyina Sirisha .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, Nepal

    Subarna Shakya

  2. Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Wang Haoxiang

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

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Sirisha, M., Sudha, S.V. (2023). A Review of Deep Learning-Based Object Detection Current and Future Perspectives. In: Shakya, S., Balas, V.E., Haoxiang, W. (eds) Proceedings of Third International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 587. Springer, Singapore. https://doi.org/10.1007/978-981-19-7874-6_69

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