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Updated: Jul 26, 2025

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A multitask model for realtime fish detection and segmentation based on YOLOv5.

QinLi Liu1, Xinyao Gong1, Jiao Li1

  • 1College of Information Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary

This study introduces a YOLOv5-based model for real-time fish detection and segmentation, enhancing intelligent fish farming. The new method achieves high accuracy in segmenting fish images, crucial for efficient aquaculture monitoring.

Keywords:
Golden crucian carp datasetMulti-taskObject detectionRealtime monitoringSemantic segmentationYOLOv5

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Area of Science:

  • Aquaculture Technology
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate fish farming and real-time monitoring are critical for developing intelligent aquaculture systems.
  • Existing instance segmentation networks, like Mask R-CNN, struggle with real-time monitoring effectiveness for fish detection and segmentation.
  • Improving fish image segmentation accuracy is vital for advancing the precision and intelligence of the fish farming industry.

Purpose of the Study:

  • To develop an accurate and efficient algorithm for real-time fish detection and segmentation.
  • To enhance the intelligence and precision of fish farming through advanced image analysis.
  • To address the limitations of current methods in real-time fish monitoring.

Main Methods:

  • Utilized YOLOv5 as the backbone network for object detection.
  • Integrated a semantic segmentation head with the YOLOv5 architecture.
  • Developed a hybrid model combining object detection and semantic segmentation for fish image analysis.

Main Results:

  • Achieved 95.4% object detection precision and 98.5% semantic segmentation accuracy on a golden crucian carp dataset.
  • Demonstrated a processing speed of 116.6 FPS on an RTX3060 graphics card.
  • Validated performance on the PASCAL VOC 2007 dataset with 73.8% detection precision, 84.3% segmentation accuracy, and 120 FPS.

Conclusions:

  • The proposed YOLOv5-based algorithm significantly improves real-time fish detection and segmentation accuracy.
  • The developed model is effective for intelligent fish farming applications, offering high precision and speed.
  • This approach represents a substantial advancement in automated aquaculture monitoring systems.