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Automated Chicken Counting in Surveillance Camera Environments Based on the Point Supervision Algorithm: LC-DenseFCN

Author

Listed:
  • Liangben Cao

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    These authors contributed to the work equally and should be regarded as co-first authors.)

  • Zihan Xiao

    (College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
    These authors contributed to the work equally and should be regarded as co-first authors.)

  • Xianghui Liao

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

  • Yuanzhou Yao

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

  • Kangjie Wu

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

  • Jiong Mu

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

  • Jun Li

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

  • Haibo Pu

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

Abstract

The density of a chicken population has a great influence on the health and growth of the chickens. For free-range chicken producers, an appropriate population density can increase their economic benefit and be utilized for estimating the economic value of the flock. However, it is very difficult to calculate the density of chickens quickly and accurately because of the complicated environmental background and the dynamic number of chickens. Therefore, we propose an automated method for quickly and accurately counting the number of chickens on a chicken farm, rather than doing so manually. The contributions of this paper are twofold: (1) we innovatively designed a full convolutional network—DenseFCN—and counted the chickens in an image using the method of point supervision, which achieved an accuracy of 93.84% and 9.27 frames per second (FPS); (2) the point supervision method was used to detect the density of chickens. Compared with the current mainstream object detection method, the higher effectiveness of this method was proven. From the performance evaluation of the algorithm, the proposed method is practical for measuring the density statistics of chickens in a farm environment and provides a new feasible tool for the density estimation of farm poultry breeding.

Suggested Citation

  • Liangben Cao & Zihan Xiao & Xianghui Liao & Yuanzhou Yao & Kangjie Wu & Jiong Mu & Jun Li & Haibo Pu, 2021. "Automated Chicken Counting in Surveillance Camera Environments Based on the Point Supervision Algorithm: LC-DenseFCN," Agriculture, MDPI, vol. 11(6), pages 1-15, May.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:6:p:493-:d:562719
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    Citations

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    Cited by:

    1. Sandor Szabo & Marta Alexy, 2022. "Practical Aspects of Weight Measurement Using Image Processing Methods in Waterfowl Production," Agriculture, MDPI, vol. 12(11), pages 1-14, November.
    2. 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.
    3. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.

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