Algae Detection Using Computer Vision and Deep Learning

TL;DR

This paper presents a fast, accurate, and cost-effective deep learning-based computer vision system for autonomous algae detection, suitable for deployment on robotic platforms to monitor water bodies in real time.

Contribution

It introduces a novel deep learning approach for algae detection that is adaptable to various hardware platforms and environments, enabling real-time monitoring.

Findings

High detection accuracy across different environments

Real-time algae monitoring capability

Compatibility with robotic platforms like USVs and UAVs

Abstract

A disconcerting ramification of water pollution caused by burgeoning populations, rapid industrialization and modernization of agriculture, has been the exponential increase in the incidence of algal growth across the globe. Harmful algal blooms (HABs) have devastated fisheries, contaminated drinking water and killed livestock, resulting in economic losses to the tune of millions of dollars. Therefore, it is important to constantly monitor water bodies and identify any algae build-up so that prompt action against its accumulation can be taken and the harmful consequences can be avoided. In this paper, we propose a computer vision system based on deep learning for algae monitoring. The proposed system is fast, accurate and cheap, and it can be installed on any robotic platforms such as USVs and UAVs for autonomous algae monitoring. The experimental results demonstrate that the proposed system can detect algae in distinct environments regardless of the underlying hardware with high accuracy and in real time.