Skip to main content
SpringerLink
Log in

A real-time detector of chicken healthy status based on modified YOLO

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In modern times, the development of an intelligent system that can automatically detect and recognize poultry diseases is vital for efficient poultry farming and for reducing human workloads. This paper presents a real-time detector that can analyze frames captured by monitoring cameras and simultaneously detect chickens and identify their healthy statuses. To overcome the challenge of chickens appearing small and having variant scales in monitoring camera frames, we integrate a scale-aware receptive field enhancement module into the YOLOv5 algorithm to enhance the receptive filed of chicken in the frames thus improving detection accuracy. In addition, we utilize a slide weighting loss function to calculate the classification loss. This helps the network to concentrate on classifying hard classified samples, leading to an improved ability to recognize the healthy statuses of chickens with greater precision. Experimental results demonstrate the proposed detector outperforms the original YOLOv5 and other one-stage object detectors, thus meeting the requirements for automated poultry health monitoring.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. SONY: Overview - Spresense. https://developer.sony.com/ja/develop/spresense/

  2. Okinda, C.: A machine vision system for early detection and prediction of sick birds: A broiler chicken model. Biosystems Engineering 188(9), 229–242 (2019)

    Article  Google Scholar 

  3. Xu, J., Zhang Kejia, G.F.: Electronic computer expert diagnosis system for chicken common heredity. China Veterinary 18(2), 40–41 (1992)

    Google Scholar 

  4. Bi Minna, Z.T., et al.: Recognition method of sick yellow feather chicken based on head features. Transactions of the Chinese Society for Agricultural Machinery 49(1), 51–57 (2018)

    Google Scholar 

  5. Zhang, N., Donahue, J.G.R.B., et al.: Part-based R-CNN for finegrained category detection. In: Proceedings of the 13th European Conference on Computer Vision (ECCV), pp. 834–949 (2014)

  6. Zhuang, X., Bi, M., Guo, J., Wu, S., Zhang, T.: Development of an early warning algorithm to detect sick broilers. Computers and Electronics in Agriculture 144, 102–113 (2018)

    Article  Google Scholar 

  7. Pereira, D.F.: Machine vision to identify broiler breeder behavior. Computers and Electronics in Agriculture 99, 194–199 (2013)

    Article  Google Scholar 

  8. Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25(2) (2012)

  9. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  10. Szegedy, C., Wei, L., Jia, Y., Sermanet, P., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015)

  11. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  12. Mbelwa, H., Mbelwa, J., Machuve, D.: Deep convolutional neural network for chicken diseases detection. Int. J. Adv. Comput. Sci. Appl. (2) (2021)

  13. Kholil, M., Waspada, H.P., Akhsani, R.: Classification of infectious diseases in chickens based on feces images using deep learning. In: 2022 International Conference on Electrical and Information Technology

  14. Pu, H., Lian, J., Fan, M.: Automatic recognition of flock behavior of chickens with convolutional neural network and kinect sensor. International Journal of Pattern Recognition and Artificial Intelligence 32(07), 1850023 (2018)

    Article  Google Scholar 

  15. Fang, C., Zhang, T., Zheng, H., Huang, J., Cuan, K.: Pose estimation and behavior classification of broiler chickens based on deep neural networks. Computers and Electronics in Agriculture 180, 105863 (2021)

    Article  Google Scholar 

  16. Nasiri, A., Yoder, J., Zhao, Y., Hawkins, S., Prado, M., Gan, H.: Pose estimation-based lameness recognition in broiler using CNN-LSTM network. Computers and Electronics in Agriculture 197, 106931 (2022)

    Article  Google Scholar 

  17. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  18. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: SSD: single shot multibox detector. In: ECCV, pp. 21–37 (2016)

  19. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6517–6525 (2017)

  20. Zhuang, X., Zhang, T.: Detection of sick broilers by digital image processing and deep learning. Biosystems Engineering 179, 106–116 (2019)

    Article  Google Scholar 

  21. Liu, H.-W., Chen, C.-H., Tsai, Y.-C., Hsieh, K.-W., Lin, H.-T.: Identifying images of dead chickens with a chicken removal system integrated with a deep learning algorithm. Sensors 21(11), 3579 (2021)

    Article  Google Scholar 

  22. Hao, H., Fang, P., Duan, E., Yang, Z., Wang, L., Wang, H.: A dead broiler inspection system for large-scale breeding farms based on deep learning. Agriculture 12(8), 1176 (2022)

    Article  Google Scholar 

  23. Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., Wang, X.: Yolo-facev2: a scale and occlusion aware face detector. arXiv preprint arXiv:2208.02019 (2022)

  24. Jocher, G.: YOLOv5. https://github.com/ultralytics/yolov5

  25. Zhang, H., Chen, C.: Design of sick chicken automatic detection system based on improved residual network. In: 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), vol. 1, pp. 2480–2485 (2020). IEEE

  26. Shi, Y., Lian, S., Siyao, Z., et al.: Recognition method of pheasant using enhanced tiny-yolov3 model. Transactions of the Chinese Society of Agricultural Engineering 13, 141–147 (2020)

    Google Scholar 

  27. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

  28. Li, Z., Zhou, F.: FSSD: Feature Fusion Single Shot Multibox Detector (2018)

  29. Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4203–4212 (2018)

  30. Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(2), 2999–3007 (2017)

    Google Scholar 

  31. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  32. Wang, J., Yuan, Y., Yu, G.: Face attention network: an effective face detector for the occluded faces. arXiv preprint arXiv:1711.07246 (2017)

  33. Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: S \(^3\) fd: Single shot scale-invariant face detector. IEEE Computer Society (2017)

  34. Najibi, M., Samangouei, P., Chellappa, R., Davis, L.S.: SSH: single stage headless face detector. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4875–4884 (2017)

  35. Yang, S., Xiong, Y., Loy, C.C., Tang, X.: Face detection through scale-friendly deep convolutional networks. arXiv preprint arXiv:1706.02863 (2017)

  36. Liu, S., Huang, D., : Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 385–400 (2018)

  37. Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., Huang, F.: DSFD: dual shot face detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5060–5069 (2019)

  38. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

  39. Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., Sun, J.: Detnet: a backbone network for object detection. arXiv preprint arXiv:1804.06215 (2018)

  40. Kong, T., Yao, A., Chen, Y., Sun, F.: Hypernet: towards accurate region proposal generation and joint object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 845–853 (2016)

  41. Singh, B., Davis, L.S.: An analysis of scale invariance in object detection snip. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3578–3587 (2018)

  42. Li, Y., Chen, Y., Wang, N., Zhang, Z.: Scale-aware trident networks for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6054–6063 (2019)

  43. Sun, C., Wu, S., Cui, T.: User selection for federated learning in a wireless environment: A process to minimize the negative effect of training data correlation and improve performance. IEEE Vehicular Technology Magazine 17(3), 26–33 (2022)

    Article  Google Scholar 

  44. Chen, M., Ren, X., Yan, Z.: Real-time indoor object detection based on deep learning and gradient harmonizing mechanism. In: 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), pp. 772–777 (2020). IEEE

  45. Cao, Y., Chen, K., Loy, C.C., Lin, D.: Prime sample attention in object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11583–11591 (2020)

  46. Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks 107, 3–11 (2018)

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. SONY China Research Laboratory, SONY Research and Development Center, Beijing, China

    Qiang Tong, Songtao Wu, Kuanhong Xu & Chen Sun

  2. College of Artificial Intelligence, China University of Petroleum, Beijing, China

    Enming Zhang

Authors
  1. Qiang Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Songtao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kuanhong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chen Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors reviewed the manuscript.

Corresponding author

Correspondence to Qiang Tong.

Ethics declarations

Conflict of interest

the authors declare no conflict of interest

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tong, Q., Zhang, E., Wu, S. et al. A real-time detector of chicken healthy status based on modified YOLO. SIViP 17, 4199–4207 (2023). https://doi.org/10.1007/s11760-023-02652-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02652-6

Keywords

Search

Navigation