PLAS-Net: Pixel-Level Area Segmentation for UAV-Based Beach Litter Monitoring

2026-04-23

AI Summary

This study addresses the limitations of conventional bounding-box-based methods for drone-based beach litter monitoring, which tend to overestimate the planar area of irregularly shaped debris and thereby compromise the accuracy of physical exposure quantification essential for reliable ecological risk assessment. To overcome this, the authors propose PLAS-Net, an instance segmentation framework that, for the first time, applies pixel-level semantic segmentation to precisely delineate the physical footprint of litter from high-resolution aerial imagery. Evaluated on a beach dataset from Koh Tao, Thailand, PLAS-Net achieves a mean average precision (mAP₅₀) of 58.7%, outperforming eleven baseline methods, and uncovers a “count–area paradox” in litter distribution. Downstream analyses further demonstrate its superior utility in plastic density estimation, ecological risk mapping, and pollution source attribution.

Technology Category

• Computer Vision: Segmentation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Accurate quantification of the physical exposure area of beach litter, rather than simple item counts, is essential for credible ecological risk assessment of marine debris. However, automated UAV-based monitoring predominantly relies on bounding-box detection, which systematically overestimates the planar area of irregular litter objects. To address this geometric limitation, we develop PLAS-Net (Pixel-level Litter Area Segmentor), an instance segmentation framework that extracts pixel-accurate physical footprints of coastal debris. Evaluated on UAV imagery from a monsoon-driven pocket beach in Koh Tao, Thailand, PLAS-Net achieves a mAP_50 of 58.7% with higher precision than eleven baseline models, demonstrating improved mask fidelity under complex coastal conditions. To illustrate how the accuracy of the masking affects the conclusions of environmental analysis, we conducted three downstream demonstrations: (i) power-law fitting of normalized plastic density (NPD) to characterize fragmentation dynamics; (ii) area-weighted ecological risk index (ERI) to map spatial pollution hotspots; and (iii) source composition analysis revealing the abundance-area paradox: fishing gear constitutes a small proportion of the total number of items, but has the largest physical area per unit item. Pixel-level area extraction can provide more valuable information for coastal monitoring compared to methods based solely on counting.

Problem

• beach litter
• UAV monitoring
• area overestimation
• ecological risk assessment
• instance segmentation

Innovation

• instance segmentation
• pixel-level area estimation
• UAV-based monitoring
• beach litter quantification
• ecological risk assessment