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Detection Method of Apple Based on Improved Lightweight YOLOv5

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Cognitive Systems and Information Processing (ICCSIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1515))

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Abstract

In order to improve the accuracy of apple detection, this essay proposes a detection method based on improved lightweight YOLOv5. By replacing the Depth Separable Convolution, the YOLOv5 algorithm goes through a lightweight modification. A visual attention mechanism model is additionally proposed, which is embedded in YOLOv5 to solve non-attention preference and parameter redundancy when extracting features in the network. The detection accuracy is therefore increased, and the computational burden brought by network parameters is reduced. Compared with the original algorithm, the detection speed can be increased optimally by 13.51%, and the average mAP can reach 95.03%. This method can basically fulfill the apple fruit identification on trees in natural environments.

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Author information

Authors and Affiliations

  1. Tsingzhan AI Research Institute, Nanjing, 211100, China

    Zhijun Li, Xuan Zhang, Xinger Feng, Yuxin Chen, Ruichen Ma, Weiqiao Wang & Shu Zhao

Authors
  1. Zhijun Li
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  2. Xuan Zhang
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  3. Xinger Feng
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  4. Yuxin Chen
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  5. Ruichen Ma
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  6. Weiqiao Wang
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  7. Shu Zhao
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Corresponding author

Correspondence to Xuan Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. National University of Defense Technology, Changsha, China

    Dewen Hu

  3. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

  4. Tsingzhan Artificial Intelligence Research Institute, Nanjing, China

    Lei Yang

  5. Tsinghua University, Beijing, China

    Huaping Liu

  6. Tsinghua University, Beijing, China

    Bin Fang

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Li, Z. et al. (2022). Detection Method of Apple Based on Improved Lightweight YOLOv5. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_22

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  • DOI: https://doi.org/10.1007/978-981-16-9247-5_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9246-8

  • Online ISBN: 978-981-16-9247-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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