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YOLOv5 versus YOLOv3 for Apple Detection

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Cyber-Physical Systems: Modelling and Intelligent Control

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 338))

Abstract

The use of the YOLOv3 and YOLOv5 algorithms for apple detection in fruit-harvesting robots are compared. It is shown that the YOLOv5 algorithm could detect apples in orchards without additional pre- and post-processing with 97.8% Recall (fruit detection rate), and 3.5% False Positive Rate (FPR). It is much better than YOLOv3 that gives 90.8% Recall and 7.8 FPR when combined with special pre- and post-processing procedures, and then 9.1% Recall and 10.0% FPR without pre- and post-processing.

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Acknowledgements

The reported study was supported by RFBR, research project 18-00-01103.

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Authors and Affiliations

  1. Financial University Under the Government of the Russian Federation, 38 Shcherbakovskaya, Moscow, 105187, Russia

    Anna Kuznetsova, Tatiana Maleva & Vladimir Soloviev

Authors
  1. Anna Kuznetsova
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  2. Tatiana Maleva
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  3. Vladimir Soloviev
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Correspondence to Anna Kuznetsova .

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Editors and Affiliations

  1. Volgograd State Technical University, Volgograd, Russia

    Alla G. Kravets

  2. Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

    Alexander A. Bolshakov

  3. Volgograd State Technical University, Volgograd, Russia

    Maxim Shcherbakov

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Kuznetsova, A., Maleva, T., Soloviev, V. (2021). YOLOv5 versus YOLOv3 for Apple Detection. In: Kravets, A.G., Bolshakov, A.A., Shcherbakov, M. (eds) Cyber-Physical Systems: Modelling and Intelligent Control. Studies in Systems, Decision and Control, vol 338. Springer, Cham. https://doi.org/10.1007/978-3-030-66077-2_28

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  • DOI: https://doi.org/10.1007/978-3-030-66077-2_28

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  • Online ISBN: 978-3-030-66077-2

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