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SDSCNet: an instance segmentation network for efficient monitoring of goose breeding conditions

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Abstract

Improve the scientific level of the goose breeding industry and help the development of intelligent agriculture. Instance Segmentation has a pivotal role when the breeders make decisions about geese breeding. It can be used for disease prevention, body size estimation and behavioural prediction, etc. However, instance segmentation requires high performance computing devices to run smoothly due to its rich output. To ameliorate this problem, this paper constructs a novel encoder-decoder module and proposes the SDSCNet model. The reasonable use of depth-separable convolution in the module reduces the number and size of model parameters and increase execution speed. Finally, SDSCNet model enables real-time identification and segmentation of individual geese with the accuracy reached 0.933.We compare this model with numerous mainstream instance segmentation models, and the final results demonstrate the excellent performance of our model.Furthermore, deploying SDSCNet model on the embedded device Raspberry Pi 4 Model B can achieve effective detection of continuous moving scenes.

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Data supporting the results of this study are not publicly available due to ongoing research on the project, etc., but may be obtained from the corresponding author(s) with reasonable justification by email, etc.

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Funding

This work was funded by the Innovation Training Program Project of Sichuan Agricultural University.

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Authors and Affiliations

  1. College of Information Engineering, Sichuan Agricultural University, No. 46 Xinkang Road, Ya’an, 625000, Sichuan Province, China

    Jiao Li, Tianyu Xie, Yijie Chen, Jianan Yuan & Xuliang Duan

  2. Institute of Collaborative Innovation, University of Macau, Avenida da Universidade, Taipa, 999077, Macau, China

    Houcheng Su

  3. School of Computation, Information and Technology, Technical University of Munich, Arcisstraße 21, Munich, 80333, Bayern, Germany

    Jianing Li

  4. College of Science, Sichuan Agricultural University, No. 46 Xinkang Road, Ya’an, 625000, Sichuan Province, China

    Kailin Jiang

Authors
  1. Jiao Li
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  4. Tianyu Xie
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  8. Xuliang Duan
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Contributions

All authors contributed to the study’s conception and design. Material preparation and data collection were performed by Jiao Li, Tianyu Xie,Yijie Chen and Jianan Yuan.Analysis was performed by Jiao Li, Houcheng Su and Jianing Li. The first draft of the manuscript was written by Jiao Li, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xuliang Duan.

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Ethics approval

The animal study protocol was approved by the Institutional Animal Care and Use Committee of Sichuan Agricultural University.

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All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or nonfinancial interest in the subject matter or materials discussed in this manuscript.

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Yijie Chen and Jianan Yuan contributed equally to this work.

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Li, J., Su, H., Li, J. et al. SDSCNet: an instance segmentation network for efficient monitoring of goose breeding conditions. Appl Intell 53, 25435–25449 (2023). https://doi.org/10.1007/s10489-023-04743-w

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