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

arXiv: https://arxiv.org/abs/2401.06157 · HTML: https://mlsanity.com/paper/2401.06157/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Robotic Detection of Marine Litter Using Deep Visual Detection Models](https://mlsanity.com/paper/1804.01079/) (2018)
- [Underwater Fish Detection using Deep Learning for Water Power Applications](https://mlsanity.com/paper/1811.01494/) (2018)
- [Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook](https://mlsanity.com/paper/2109.14737/) (2021)
- [Applications of Deep Learning in Fish Habitat Monitoring: A Tutorial and Survey](https://mlsanity.com/paper/2206.05394/) (2022)

## Abstract (arXiv)

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