Understanding the pathways through which plastics travel from terrestrial environments into the world’s oceans is a critical step in addressing the escalating crisis of marine plastic pollution. Rivers have emerged as pivotal conduits for this transport, channeling vast quantities of plastic waste into seas and oceans globally. Traditional monitoring methods, typically reliant on manual observation, face significant challenges, particularly when it comes to capturing data under extreme environmental conditions such as floods. Responding to these hurdles, a multidisciplinary research team has developed an innovative software system that employs cutting-edge image processing and artificial intelligence technologies to revolutionize the continuous monitoring and quantification of plastics transported in riverine environments.
This new system integrates three advanced computational techniques to analyze video data captured from river surfaces in real time. At the core of the velocity measurement component lies template matching, an image recognition technique that identifies and tracks motion by comparing segments of sequential video frames to detect flow patterns. This enables precise quantification of river surface flow velocity, a fundamental parameter influencing plastic transport dynamics. Template matching operates by overlaying a pre-defined template onto consecutive video frames to find the best match, thus deducing movement over time with high spatial and temporal resolution.
Complementing flow velocity measurements is the deployment of the latest version of the YOLO (You Only Look Once) object detection algorithm, YOLOv8. This deep learning model is capable of swiftly detecting multiple object classes within images and videos while maintaining remarkable accuracy. In this context, YOLOv8 has been trained to identify and categorize floating plastic debris into four distinct types. Its real-time detection capability makes it ideally suited for analyzing large volumes of continuously captured river footage, enabling granular classification of plastics by form and type, which is essential for source identification and waste management evaluation.
Further enhancing the system’s capabilities, an advanced object tracking algorithm known as Deep SORT (Simple Online and Realtime Tracking with deep learning-based appearance descriptors) has been integrated to maintain the identity of detected plastic pieces across video frames. Deep SORT extends upon traditional SORT methodologies by incorporating sophisticated deep neural network features that improve robust identification even in the presence of occlusions or overlapping objects. This tracking mechanism allows the software to follow individual plastic items as they move through the river, generating detailed movement trajectories essential for calculating transport volumes.
By synthesizing the data from flow velocity measurements and plastic tracking, the software automatically computes the volume of floating plastics passing through a river segment per unit time. This quantification is performed not only in terms of counts but also by mass estimates, providing comprehensive insight into the scale of plastic pollution. The automation embedded in this system facilitates continuous and simultaneous monitoring across multiple sites, representing a significant leap forward from labor-intensive manual monitoring methods constrained by safety issues and limited temporal coverage.
The capacity to monitor under a variety of conditions, including during high-flow and flood events, distinguishes this approach from previous efforts. Floods, which often exacerbate plastic transport and redistribute accumulated debris, have traditionally posed challenges to field researchers due to safety and accessibility concerns. The remote, video-based monitoring enabled by this software mitigates such risks and yields unprecedented continuous data streams vital for understanding episodic plastic fluxes and their impacts downstream.
An additional critical feature of this software is its ability to differentiate between types of plastics based on their classifications from YOLOv8. This granularity supports more direct and targeted evaluation of upstream source reduction strategies and waste management policies. By accurately identifying which plastic categories dominate riverine transport at various times and locations, stakeholders can prioritize interventions and measure their efficacy with data-driven confidence.
Looking ahead, the developers plan to embed this technology into the Plastic River Monitoring System (PRIMOS), a collaborative initiative with industrial partner Yachiyo Engineering Co., Ltd. PRIMOS aims to facilitate broad-scale deployment of the system in real-world river environments, enabling detailed basin-wide assessments. The software’s integration into this platform promises to yield invaluable data streams for environmental policymakers and researchers seeking to quantify land-to-sea plastic fluxes comprehensively.
This research initiative aligns closely with international environmental commitments such as the “Osaka Blue Ocean Vision” formulated during the 2019 G20 Summit in Osaka, which targets zero additional marine plastic pollution by 2050. Precise, real-time monitoring technologies like this AI-driven software are poised to play an essential role in tracking progress toward these ambitious goals, guiding adaptive policies grounded in empirical evidence.
The multidisciplinary nature of this approach—melding environmental science, computer vision, and AI—reflects a broader shift towards leveraging technological innovation to address complex ecological challenges. By demonstrating the practical application of state-of-the-art image recognition and tracking technologies in environmental monitoring, this work sets a precedent for future studies and initiatives aimed at sustainable management of plastic pollution.
Ultimately, this pioneering system offers a transformative tool for stakeholders engaged in plastic pollution mitigation, from local environmental agencies to international organizations. The capacity to continuously and accurately monitor plastic transport in rivers under diverse conditions will deepen scientific understanding, improve policymaking, and bolster collective efforts toward a cleaner and more sustainable global environment.
Subject of Research: Plastic transport monitoring in riverine environments using AI and image analysis
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Image Credits: Tomoya Kataoka (Ehime University)
Keywords: Engineering, Computer science, Environmental sciences, Remote sensing, Technology, Earth sciences, Environmental methods
