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Updated: Apr 11, 2026

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Protocol for Microplastics Sampling on the Sea Surface and Sample Analysis

Published on: December 16, 2016

Real-Time Detection of Microplastics Using an AI Camera.

Md Abdul Baset Sarker¹, Masudul H Imtiaz¹, Thomas M Holsen²

¹Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13699, USA.

Sensors (Basel, Switzerland)

|July 13, 2024

Summary

Researchers developed an AI-powered camera system to detect and track microplastics (MPs) in water. This innovative technology achieved high precision in both lab and field tests, aiding pollution monitoring.

Keywords:

DeepSORT YOLOv5 artificial intelligence (AI)environmental monitoring freshwater ecosystems machine vision microplastics (MPs)object detection underwater detection

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Area of Science:

Environmental Science
Computer Science
Engineering

Background:

Microplastic (MP) pollution is a global environmental crisis affecting aquatic ecosystems.
Current MP detection technologies are insufficient for real-time monitoring and analysis.
There is a critical need for advanced tools to quantify MP presence and movement.

Purpose of the Study:

To develop and evaluate an AI-driven camera sensor system for detecting and measuring microplastics (MPs).
To assess the performance of different camera configurations (fixed-focus 2D, autofocus 2D/3D) for MP detection.
To enable real-time size and velocity measurements of moving MPs.

Main Methods:

Implemented a computer vision and artificial intelligence (AI) approach for MP detection.
Utilized a YOLOv5 object detection model for identifying MPs in images.
Employed DeepSORT algorithm for tracking MPs across sequential video frames.
Compared performance across three distinct camera systems in laboratory and field settings.

Main Results:

Achieved 97% precision in MP counting during real-time laboratory flume testing.
Attained 96% precision in MP detection during field testing in a local river.
Demonstrated the system's capability to detect and track MPs in motion, measuring size and velocity.

Conclusions:

AI and computer vision offer a promising solution for effective microplastic detection.
The developed camera sensor system provides foundational insights for environmental monitoring.
This technology can significantly contribute to strategies for managing and mitigating microplastic pollution.