A Deep Learning Approach to Assist Sustainability of Demersal Trawling Operations

Maria Sokolova,Roberto Galeazzi,Ludvig Ahm Krag,Fletcher Thompson,Patrizio Mariani,Adrià Mompó Alepuz

doi:10.3390/su132212362

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.

Highlights

To account for this, we explored the use of cloud augmentation (“Cloud”), which introduced random clumps of cloud-like patterns with varying sizes and colors that resembled the sediment shapes found during trawling
We have demonstrated that the developed in-trawl observation system and the automated catch description approach is effective in this fishery
We present the first solution for automated catch description for the commercial demersal trawl fishery

Summary

Introduction

To mitigate catch and subsequent discard of unwanted species and sizes, ambitious management plans such as the EU Common Fisheries Policy landing obligation have been implemented, forcing fishers to declare all catches of listed species and count them against their quota [3]. The management plans are combined with technical regulations aiming at improving the gears size and species selectivity through mesh size regulations, trawl modifications and bycatch reduction devices. Despite these measures, catch of unwanted sizes and species still challenge these fisheries [2,4].

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Discussion

Conclusion