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A Fast Neural Network Based on Attention Mechanisms for Detecting Field Flat Jujube

Author

Listed:
  • Shilin Li

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China)

  • Shujuan Zhang

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China)

  • Jianxin Xue

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China)

  • Haixia Sun

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China)

  • Rui Ren

    (College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030800, China)

Abstract

The efficient identification of the field flat jujube is the first condition to realize its automated picking. Consequently, a lightweight algorithm of target identification based on improved YOLOv5 (you only look once) is proposed to meet the requirements of high-accuracy and low-complexity. At first, the proposed method solves the imbalance of data distribution by improving the methods of data enhancement. Then, to improve the accuracy of the model, we adjust the structure and the number of the Concentrated-Comprehensive Convolution Block modules in the backbone network, and introduce the attention mechanisms of Efficient Channel Attention and Coordinate Attention. On this basis, this paper makes lightweight operations by using the Deep Separable Convolution to reduce the complexity of the model. Ultimately, the Complete Intersection over Union loss function and the non-maximum suppression of Distance Intersection over Union are used to optimize the loss function and the post-processing process, respectively. The experimental results show that the mean average precision of improved network reaches 97.4%, which increases by 1.7% compared with the original YOLOv5s network; and, the parameters, floating point of operations, and model size are compressed to 35.39%, 51.27%, and 37.5% of the original network, respectively. The comparison experiments are conducted around the proposed method and the common You Only Look Once target detection algorithms. The experimental results show that the mean average precision of the proposed method is 97.4%, which is higher than the 90.7%, 91.7%, and 88.4% of the YOLOv3, YOLOv4, and YOLOx-s algorithms, and the model size decreased to 2.3%, 2.2%, and 15.7%, respectively. The improved algorithm realizes a reduction of complexity and an increase in accuracy, it can be suitable for lightweight deployment to a mobile terminal at a later stage, and it provides a certain reference for the visual detection of picking robots.

Suggested Citation

  • Shilin Li & Shujuan Zhang & Jianxin Xue & Haixia Sun & Rui Ren, 2022. "A Fast Neural Network Based on Attention Mechanisms for Detecting Field Flat Jujube," Agriculture, MDPI, vol. 12(5), pages 1-19, May.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:5:p:717-:d:818420
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    References listed on IDEAS

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    1. Tej Bahadur Shahi & Chiranjibi Sitaula & Arjun Neupane & William Guo, 2022. "Fruit classification using attention-based MobileNetV2 for industrial applications," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-21, February.
    2. Monika Hooda & Chhavi Rana & Omdev Dahiya & Jayashree Premkumar Shet & Bhupesh Kumar Singh & Vijay Kumar, 2022. "Integrating LA and EDM for Improving Students Success in Higher Education Using FCN Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, March.
    3. Yue Gu & Shucai Wang & Yu Yan & Shijie Tang & Shida Zhao, 2022. "Identification and Analysis of Emergency Behavior of Cage-Reared Laying Ducks Based on YoloV5," Agriculture, MDPI, vol. 12(4), pages 1-16, March.
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    Cited by:

    1. Yutan Wang & Zhenwei Xing & Liefei Ma & Aili Qu & Junrui Xue, 2022. "Object Detection Algorithm for Lingwu Long Jujubes Based on the Improved SSD," Agriculture, MDPI, vol. 12(9), pages 1-17, September.

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