Analysis based on object detection algorithms

Puran Lyu

doi:10.54254/2755-2721/20/20231066

Research Article

Open access

Published on 23 October 2023

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Lyu,P. (2023). Analysis based on object detection algorithms. Applied and Computational Engineering,20,33-39.

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Analysis based on object detection algorithms

Puran Lyu *^,1,

¹ New York University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/20/20231066

Abstract

With the increasing demand for intelligent systems capable of comprehending visual information, the discipline of image object detection has experienced rapid expansion. Despite the fact that numerous methods have been proposed, the existing literature lacks exhaustive analyses and summaries of these methods. This paper seeks to address this deficiency by providing a thorough overview and analysis of image object detection techniques. This paper analyzes and discusses traditional methods and deep learning-based methods, with a focus on analyzing the current state and shortcomings of traditional methods. Further discussion is given to deep network-based object detection methods, mainly through a comparative analysis of two-stage and one-stage methods. The basic performance of the You Look Only Once (YOLO) series methods is highlighted. The contribution of large-scale datasets and evaluation metrics to the advancement of the state of the art is also examined. This comprehensive analysis is a useful reference for researchers who aim to contribute to the continual progress of image object detection.

Keywords

object detection, deep learning, YOLO, evaluation metrics

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References

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Cite this article

Lyu,P. (2023). Analysis based on object detection algorithms. Applied and Computational Engineering,20,33-39.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

Conference website: https://2023.confcds.org/

ISBN：978-1-83558-031-8(Print) / 978-1-83558-032-5(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Marwan Omar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.20

ISSN：2755-2721(Print) / 2755-273X(Online)

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