A Deep Learning Approach to Assist Sustainability of Demersal Trawling Operations

Maria Sokolova, Adrià Mompó Alepuz, Fletcher Thompson, Patrizio Mariani, Roberto Galeazzi and Ludvig Ahm Krag
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Maria Sokolova: National Institute of Aquatic Resources, Technical University of Denmark, 9850 Hirtshals, Denmark
Adrià Mompó Alepuz: Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Fletcher Thompson: National Institute of Aquatic Resources, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Patrizio Mariani: National Institute of Aquatic Resources, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Roberto Galeazzi: Automation and Control Group, Department of Electrical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Ludvig Ahm Krag: National Institute of Aquatic Resources, Technical University of Denmark, 9850 Hirtshals, Denmark

Sustainability, 2021, vol. 13, issue 22, 1-18

Abstract: Bycatch in demersal trawl fisheries challenges their sustainability despite the implementation of the various gear technical regulations. A step towards extended control over the catch process can be established through a real-time catch monitoring tool that will allow fishers to react to unwanted catch compositions. In this study, for the first time in the commercial demersal trawl fishery sector, we introduce an automated catch description that leverages state-of-the-art region based convolutional neural network (Mask R-CNN) architecture and builds upon an in-trawl novel image acquisition system. The system is optimized for applications in Nephrops fishery and enables the classification and count of catch items during fishing operation. The detector robustness was improved with augmentation techniques applied during training on a custom high-resolution dataset obtained during extensive demersal trawling. The resulting algorithms were tested on video footage representing both the normal towing process and haul-back conditions. The algorithm obtained an F-score of 0.79. The resulting automated catch description was compared with the manual catch count showing low absolute error during towing. Current practices in demersal trawl fisheries are carried out without any indications of catch composition nor whether the catch enters the fishing gear. Hence, the proposed solution provides a substantial technical contribution to making this type of fishery more targeted, paving the way to further optimization of fishing activities aiming at increasing target catch while reducing unwanted bycatch.

Keywords: deep learning; innovation in fisheries; digitized fishery; automated catch description (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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