Cognitive Edge Device (CED) for Real-Time Environmental Monitoring in Aquatic Ecosystems

TL;DR

This paper presents a real-time aquatic monitoring system using a cognitive edge device and YOLO models to detect invasive crayfish and plastic debris, aiding ecosystem protection.

Contribution

It introduces the Cognitive Edge Device platform and provides annotated datasets for crayfish and plastic detection, along with evaluating YOLO variants for this task.

Findings

YOLOv5s achieved 0.90 mAP@0.5 in detection accuracy.

The system enables real-time monitoring of invasive species and debris.

The datasets are publicly available for further research.

Abstract

Invasive signal crayfish have a detrimental impact on ecosystems. They spread the fungal-type crayfish plague disease (Aphanomyces astaci) that is lethal to the native white clawed crayfish, the only native crayfish species in Britain. Invasive signal crayfish extensively burrow, causing habitat destruction, erosion of river banks and adverse changes in water quality, while also competing with native species for resources leading to declines in native populations. Moreover, pollution exacerbates the vulnerability of White-clawed crayfish, with their populations declining by over 90%. To safeguard aquatic ecosystems, it is imperative to address the challenges posed by invasive species and pollution in aquatic ecosystem's. This article introduces the Cognitive Edge Device (CED) computing platform for the detection of crayfish and plastic. It also presents two publicly available underwater datasets, annotated with sequences of crayfish and aquatic plastic debris. Four You Only Look Once (YOLO) variants were trained and evaluated for crayfish and plastic object detection. YOLOv5s achieved the highest detection accuracy, with an mAP@0.5 of 0.90, and achieved the best precision