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An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation

Author

Listed:
  • Kailin Jiang

    (College of Science, Sichuan Agricultural University, Ya’an 625000, China
    These authors contributed equally to this work.)

  • Tianyu Xie

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Rui Yan

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Xi Wen

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Danyang Li

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    These authors contributed equally to this work.)

  • Hongbo Jiang

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Ning Jiang

    (College of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China)

  • Ling Feng

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Xuliang Duan

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China)

  • Jianjun Wang

    (College of Science, Sichuan Agricultural University, Ya’an 625000, China)

Abstract

Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to double counting and omission. In this regard, this paper uses deep learning algorithms to achieve real-time monitoring of the number of dense hemp duck flocks and to promote the development of the intelligent farming industry. We constructed a new large-scale hemp duck object detection image dataset, which contains 1500 hemp duck object detection full-body frame labeling and head-only frame labeling. In addition, this paper proposes an improved attention mechanism YOLOv7 algorithm, CBAM-YOLOv7, adding three CBAM modules to the backbone network of YOLOv7 to improve the network’s ability to extract features and introducing SE-YOLOv7 and ECA-YOLOv7 for comparison experiments. The experimental results show that CBAM-YOLOv7 had higher precision, and the recall, mAP@0.5, and mAP@0.5:0.95 were slightly improved. The evaluation index value of CBAM-YOLOv7 improved more than those of SE-YOLOv7 and ECA-YOLOv7. In addition, we also conducted a comparison test between the two labeling methods and found that the head-only labeling method led to the loss of a high volume of feature information, and the full-body frame labeling method demonstrated a better detection effect. The results of the algorithm performance evaluation show that the intelligent hemp duck counting method proposed in this paper is feasible and can promote the development of smart reliable automated duck counting.

Suggested Citation

  • Kailin Jiang & Tianyu Xie & Rui Yan & Xi Wen & Danyang Li & Hongbo Jiang & Ning Jiang & Ling Feng & Xuliang Duan & Jianjun Wang, 2022. "An Attention Mechanism-Improved YOLOv7 Object Detection Algorithm for Hemp Duck Count Estimation," Agriculture, MDPI, vol. 12(10), pages 1-18, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1659-:d:938212
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    Citations

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    Cited by:

    1. Mehrnaz Farokhnejad Afshar & Zahra Shirmohammadi & Seyyed Amir Ali Ghafourian Ghahramani & Azadeh Noorparvar & Ali Mohammad Afshin Hemmatyar, 2023. "An Efficient Approach to Monocular Depth Estimation for Autonomous Vehicle Perception Systems," Sustainability, MDPI, vol. 15(11), pages 1-21, May.
    2. Dimitre D. Dimitrov, 2023. "Internet and Computers for Agriculture," Agriculture, MDPI, vol. 13(1), pages 1-7, January.
    3. Wael M. Elmessery & Joaquín Gutiérrez & Gomaa G. Abd El-Wahhab & Ibrahim A. Elkhaiat & Ibrahim S. El-Soaly & Sadeq K. Alhag & Laila A. Al-Shuraym & Mohamed A. Akela & Farahat S. Moghanm & Mohamed F. A, 2023. "YOLO-Based Model for Automatic Detection of Broiler Pathological Phenomena through Visual and Thermal Images in Intensive Poultry Houses," Agriculture, MDPI, vol. 13(8), pages 1-21, July.
    4. Hongyun Hao & Peng Fang & Wei Jiang & Xianqiu Sun & Liangju Wang & Hongying Wang, 2022. "Research on Laying Hens Feeding Behavior Detection and Model Visualization Based on Convolutional Neural Network," Agriculture, MDPI, vol. 12(12), pages 1-12, December.
    5. Yaxin Wang & Xinyuan Liu & Fanzhen Wang & Dongyue Ren & Yang Li & Zhimin Mu & Shide Li & Yongcheng Jiang, 2023. "Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    6. Jiajun Lai & Yun Liang & Yingjie Kuang & Zhannan Xie & Hongyuan He & Yuxin Zhuo & Zekai Huang & Shijie Zhu & Zenghang Huang, 2023. "IO-YOLOv5: Improved Pig Detection under Various Illuminations and Heavy Occlusion," Agriculture, MDPI, vol. 13(7), pages 1-18, July.

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