Skip to main content
SpringerLink
Log in

A novel detection model and platform for dead juvenile fish from the perspective of multi-task

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The cultivation stage of juvenile fish is the starting point of the whole cultivation process. In this stage, the vitality of juvenile fish is weak and they are extremely vulnerable to environmental stress and death. Therefore, there is an urgent need for an automated method to replace the aquaculture personnel to achieve the growth monitoring of juvenile fish in the cultivation stage. However, the dead juvenile fish are generally small, and there are interference signals with high similarity to the color of the dead fish, such as reflection and foam, on the water surface of the aquaculture area. To solve this problem, this study constructs dead fish detection model from two perspectives of improving model accuracy and realizing model lightweight. First, the dead fish detection data set is constructed using visual technology. Secondly, the Efficient Channel Attention module (ECA) is used to strengthen the feature auxiliary branch of YOLOv4 backbone feature extraction network output (E-YOLOv4), the E-YOLOv4 dead fish detection model is constructed (mAP = 96.91%, model-size = 244 M). In addition, this study replaced YOLOv4’s cross stage partial network (CSPDarkNet53) with Densenet169 (D-YOLOv4), the D-YOLOv4 dead fish detection model is constructed (mAP = 95.93%, model-size = 84.7 M). The results show that E-YOLOV4 and D-YOLOV4 are superior to YOLOV4 in model effect and model-size. Finally, Python+PyQt5 is used to build a dead fish monitoring platform, and the above model is deployed on the platform to achieve real-time monitoring of dead fish in the breeding process. The dead fish monitoring platform established in this study can not only assist the aquaculture personnel in daily aquaculture supervision, but also assist the water quality sensor to jointly monitor the safety of the growth environment of juvenile fish. The platform is simple in design and has certain practical application value.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Anas O, Wageeh Y, Mohamed HED et al (2020) Detecting abnormal fish behavior using motion trajectories in ubiquitous environments. Procedia Comput Sci 175:141–148. https://doi.org/10.1016/j.procs.2020.07.023

    Article  Google Scholar 

  2. Beyan C, Fisher RB (2013) Detecting abnormal fish trajectories using clustered and labeled data. 2013 IEEE Int Conf Image Process ICIP 2013 - Proc 1476–1480. https://doi.org/10.1109/ICIP.2013.6738303

  3. Cheng S, Zhao K, Zhang D (2019) Abnormal water quality monitoring based on visual sensing of three-dimensional motion behavior of fish. Symmetry (Basel) 11:1–20. https://doi.org/10.3390/sym11091179

    Article  ADS  Google Scholar 

  4. Goldstein ED, Sponaugle S (2020) Juvenile reef fish growth and survival related to subregional patterns of primary production. Mar Biol 167:1–10. https://doi.org/10.1007/s00227-019-3627-9

    Article  Google Scholar 

  5. Hu J, Zhao D, Zhang Y et al (2021) Real-time nondestructive fish behavior detecting in mixed polyculture system using deep-learning and low-cost devices. Expert Syst Appl 178:115051. https://doi.org/10.1016/j.eswa.2021.115051

    Article  Google Scholar 

  6. Hu Z, Li XH, Xie XY, Zhao YC (2022) Abnormal Behavior Recognition of Underwater Fish Body Based on C3D Model. ACM Int Conf Proceeding Ser 92–97. https://doi.org/10.1145/3523150.3523165

  7. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. pp 2261–2269

  8. Jalal A, Salman A, Mian A et al (2020) Fish detection and species classification in underwater environments using deep learning with temporal information. Ecol Inform 57:101088. https://doi.org/10.1016/j.ecoinf.2020.101088

    Article  Google Scholar 

  9. Jie C, Yingying S, Junhui W, et al (2019) Intelligent Control and Management System for Recirculating Aquaculture. 2019 IEEE 2nd Int Conf Electron Commun Eng ICECE 2019 438–443. https://doi.org/10.1109/ICECE48499.2019.9058567

  10. Konovalov DA, Saleh A, Bradley M, et al (2019) Underwater Fish Detection with Weak Multi-Domain Supervision. Proc Int Jt Conf Neural Networks 2019-July:14–19. https://doi.org/10.1109/IJCNN.2019.8851907

  11. Li D, Li C (2020) Intelligent aquaculture. J World Aquac Soc 51:808–814. https://doi.org/10.1111/jwas.12736

    Article  ADS  Google Scholar 

  12. Li X, Shang M, Hao J, Yang Z (2016) Accelerating fish detection and recognition by sharing CNNs with objectness learning. Ocean 2016 - Shanghai 0–4. https://doi.org/10.1109/OCEANSAP.2016.7485476

  13. Li X, Shang M, Qin H, Chen L (2016) Fast accurate fish detection and recognition of underwater images with Fast R-CNN. Ocean 2015 - MTS/IEEE Washingt 1–5. https://doi.org/10.23919/oceans.2015.7404464

  14. Li X, Tang Y, Gao T (2017) Deep but lightweight neural networks for fish detection. Ocean 2017 - Aberdeen 2017-Octob:1–5. https://doi.org/10.1109/OCEANSE.2017.8084961

  15. Li X, Hao Y, Zhang P et al (2022) A novel automatic detection method for abnormal behavior of single fish using image fusion. Comput Electron Agric 203:107435. https://doi.org/10.1016/j.compag.2022.107435

    Article  Google Scholar 

  16. Liu W, Anguelov D, Erhan D, et al (2016) SSD: Single shot multibox detector. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 9905 LNCS:21–37. https://doi.org/10.1007/978-3-319-46448-0_2

  17. Matić-Skoko S, Vrdoljak D, Uvanović H et al (2020) Early evidence of a shift in juvenile fish communities in response to conditions in nursery areas. Sci Rep 10:1–16. https://doi.org/10.1038/s41598-020-78181-w

    Article  CAS  Google Scholar 

  18. Qian ZM, Wang SH, Cheng XE, Chen YQ (2016) An effective and robust method for tracking multiple fish in video image based on fish head detection. BMC Bioinforma 17:1–11. https://doi.org/10.1186/s12859-016-1138-y

    Article  Google Scholar 

  19. Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. Comput Vis Pattern Recognit

  20. Salman A, Maqbool S, Khan AH et al (2019) Real-time fish detection in complex backgrounds using probabilistic background modelling. Ecol Inform 51:44–51. https://doi.org/10.1016/j.ecoinf.2019.02.011

    Article  Google Scholar 

  21. Salman A, Siddiqui SA, Shafait F et al (2020) Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system. ICES J Mar Sci 77:1295–1307. https://doi.org/10.1093/icesjms/fsz025

    Article  Google Scholar 

  22. Scoulding B, Maguire K, Orenstein EC (2022) Evaluating automated benthic fish detection under variable conditions. ICES J Mar Sci 79:2204–2216. https://doi.org/10.1093/icesjms/fsac166

    Article  Google Scholar 

  23. Sung M, Yu SC, Girdhar Y (2017) Vision based real-time fish detection using convolutional neural network. Ocean 2017 - Aberdeen 2017-Octob:1–6. https://doi.org/10.1109/OCEANSE.2017.8084889

  24. Thida M, Eng HL, Chew BF (2009) Automatic analysis of fish behaviors and abnormality detection. Proc 11th IAPR Conf Mach Vis Appl MVA 2009 278–282

  25. Wang C, Liao HM (2020) YOLOv4: Optimal Speed and Accuracy of Object Detection. Comput Vis Pattern Recognit

  26. Wang Q, Wu B, Zhu P, et al (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 11531–11539. https://doi.org/10.1109/CVPR42600.2020.01155

  27. Wang C, Li Z, Wang T, et al (2021) Intelligent fish farm—the future of aquaculture. Springer International Publishing

  28. Wang H, Zhang S, Zhao S et al (2022) Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++. Comput Electron Agric 192:106512. https://doi.org/10.1016/j.compag.2021.106512

    Article  Google Scholar 

  29. Wang C-Y, Bochkovskiy A, Liao H-YM (2022) YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Comput Vis Pattern Recognit 1–15. https://doi.org/10.48550/arXiv.2207.02696

  30. Xu W, Matzner S (2018) Underwater fish detection using deep learning for water power applications. Proc - 2018 Int Conf Comput Sci Comput Intell CSCI 2018 313–318. https://doi.org/10.1109/CSCI46756.2018.00067

  31. Xu W, Zhu Z, Ge F et al (2020) Analysis of behavior trajectory based on deep learning in ammonia environment for fish. Sensors (Switzerland) 20:1–11. https://doi.org/10.3390/s20164425

    Article  CAS  Google Scholar 

  32. Yang L, Liu Y, Yu H, et al (2021) Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: a review. Arch Comput Methods Eng 28:2785–2816. https://doi.org/10.1007/s11831-020-09486-2

  33. Yu G, Wang L, Hou M et al (2020) An adaptive dead fish detection approach using SSD-MobileNet. Proc - 2020 Chinese Autom Congr CAC, pp 1973–1979. https://doi.org/10.1109/CAC51589.2020.9326648

  34. Zhang P, Li D (2022) EPSA-YOLO-V5s: A novel method for detecting the survival rate of rapeseed in a plant factory based on multiple guarantee mechanisms. Comput Electron Agric 193:106714. https://doi.org/10.1016/j.compag.2022.106714

    Article  Google Scholar 

  35. Zhao S, Zhang S, Lu J et al (2022) A lightweight dead fish detection method based on deformable convolution and YOLOV4. Comput Electron Agric 198:107098–107109. https://doi.org/10.1016/j.compag.2022.107098

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the editor and reviewers for their valuable input, time, and suggestions to improve the quality of the manuscript. This work was supported by the talent cultivation and development support plan team construction funds - national innovation team leading professor class A, Study on mechanism and method of rapid detection of trace toxic nitrogen in aquaculture water based on SERS light pole [Grant No.2018QC188] and the National Natural Science Foundation of China “Study on characteristics recognition and behavior analysis of swimming fish feeding population based on machine vision”, Yantai Industrial Leading Talent Project, National Marine Ranch Information Platform Design [Grant No.62076244].

Author information

Author notes

  1. Pan Zhang and Jishu Zheng contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China

    Pan Zhang & Daoliang Li

  2. Key Laboratory of Smart Farming Technologies for Aquatic Animal and Livestock, Ministry of Agriculture and Rural Affairs, China Agriculture University, Beijing, 100083, China

    Pan Zhang & Daoliang Li

  3. Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture China Agriculture University, Beijing, 100083, China

    Pan Zhang & Daoliang Li

  4. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Pan Zhang & Daoliang Li

  5. Chongqing Academy of Agricultural Sciences, Chongqing, 400000, China

    Jishu Zheng, Lihong Gao, Ping Li, Hanwei Long & Hongbo Liu

Authors
  1. Pan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jishu Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lihong Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hanwei Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongbo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daoliang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Pan Zhang: Methodology, Investigation, Conceptualization, Software, Writing-original draft, Visualization, Jishu Zheng: Resources, Investigation, Methodology, Lihong Gao: Resources, Project administration, Ping Li: Resources, Project administration, Hanwei Long: Supervision, Hongbo Liu: Validation, Daoliang Li: Conceptualization, Writing -review & editing, Supervision, Funding acquisition.

Corresponding author

Correspondence to Daoliang Li.

Ethics declarations

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Zhang, P., Zheng, J., Gao, L. et al. A novel detection model and platform for dead juvenile fish from the perspective of multi-task. Multimed Tools Appl 83, 24961–24981 (2024). https://doi.org/10.1007/s11042-023-16370-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16370-1

Keywords

Search

Navigation