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Edge computing based real-time Nephrops (Nephrops norvegicus) catch estimation in demersal trawls using object

Ercan Avsar1, Jordan P Feekings2, Ludvig Ahm Krag2

  • 1Section for Fisheries Technology, Institute of Aquatic Resources, Technical University of Denmark, Hirtshals, Denmark. erca@aqua.dtu.dk.

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A new real-time underwater video system uses object detection to count Nephrops in demersal trawls. This technology enhances fishery sustainability and economic performance by providing immediate catch data.

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Area of Science:

  • Marine biology
  • Fisheries science
  • Computer vision

Background:

  • Demersal trawl fisheries face challenges with delayed catch data, impacting sustainability and economics.
  • In-trawl cameras offer real-time catch observation potential.
  • Processing this data is crucial for improving fishery management.

Purpose of the Study:

  • To develop and evaluate a real-time underwater video processing system for counting Nephrops individuals.
  • To optimize system performance for accurate and rapid catch assessment.

Main Methods:

  • Utilized object detection and tracking on an edge device (NVIDIA Jetson AGX Orin).
  • Tested seven state-of-the-art YOLO models and evaluated four frame skipping techniques.
  • Focused on achieving simultaneous real-time processing and accurate counting.

Main Results:

  • The YOLOv8s model combined with adaptive frame skipping achieved 97.47 FPS.
  • Attained a correct count rate of 82.57% and an F-score of 0.86.
  • Demonstrated the feasibility of real-time processing for demersal trawl fisheries.

Conclusions:

  • The developed system provides real-time catch information for Nephrops-directed fisheries.
  • This technology can significantly improve the sustainability and economic viability of these fisheries.
  • Real-time data processing is key to addressing drawbacks in traditional trawl fisheries.