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Machine learning system automates fish discard quantification

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CatchMonitor, a machine learning system designed to automatically quantify discarded fish from video footage on fishing trawlers. This computer vision system improves species identification accuracy through semi-supervised learning and utilizes a robust object tracking approach. The system's performance is benchmarked against manual quantification by human analysts. AI

IMPACT This system could improve the accuracy and efficiency of fisheries data collection, aiding in sustainable fishing practices.

RANK_REASON The cluster contains an academic paper detailing a new machine learning system. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning system automates fish discard quantification

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16 / 100
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The cluster contains an academic paper detailing a new machine learning system. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Geoff French, Michal Mackiewicz, Mark Fisher, Helen Holah, Rebecca Lamb ·

    CatchMonitor: a machine learning system for automated fish discard quantification

    arXiv:2609.15484v1 Announce Type: new Abstract: We report on the continued development of CatchMonitor, resulting in a prototype computer vision system designed to automatically quantify discarded fish from video footage collected from Remote Electronic Monitoring (REM) systems o…

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