Efficient plastic detection in coastal areas with selected spectral bands Article Swipe
Ámbar Pérez-García
,
Tim van Emmerik
,
Aser Mata
,
Paolo Tasseron
,
José F. López
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.marpolbul.2024.116914
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.marpolbul.2024.116914
Marine plastic pollution poses significant ecological, economic, and social challenges, necessitating innovative detection, management, and mitigation solutions. Spectral imaging and optical remote sensing have proven valuable tools in detecting and characterizing macroplastics in aquatic environments. Despite numerous studies focusing on bands of interest in the shortwave infrared spectrum, the high cost of sensors in this range makes it difficult to mass-produce them for long-term and large-scale applications. Therefore, we present the assessment and transfer of various machine learning models across four datasets to identify the key bands for detecting and classifying the most prevalent plastics in the marine environment within the visible and near-infrared (VNIR) range. Our study uses four different databases ranging from virgin plastics under laboratory conditions to weather plastics under field conditions. We used Sequential Feature Selection (SFS) and Random Forest (RF) models for the optimal band selection. The significance of homogeneous backgrounds for accurate detection is highlighted by a 97 % accuracy, and successful band transfers between datasets (87 %-91 %) suggest the feasibility of a sensor applicable across various scenarios. However, the model transfer requires further training for each specific dataset to achieve optimal accuracy. The results underscore the potential for broader application with continued refinement and expanded training datasets. Our findings provide valuable information for developing compelling and affordable detection sensors to address plastic pollution in coastal areas. This work paves the way towards enhancing the accuracy of marine litter detection and reduction globally, contributing to a sustainable future for our oceans.
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- Type
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- Landing Page
- https://doi.org/10.1016/j.marpolbul.2024.116914
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All OpenAlex metadata
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https://doi.org/10.1016/j.marpolbul.2024.116914Digital Object Identifier
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Efficient plastic detection in coastal areas with selected spectral bandsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-09-07Full publication date if available
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Ámbar Pérez-García, Tim van Emmerik, Aser Mata, Paolo Tasseron, José F. LópezList of authors in order
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https://doi.org/10.1016/j.marpolbul.2024.116914Publisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.marpolbul.2024.116914Direct OA link when available
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2025: 5, 2024: 3Per-year citation counts (last 5 years)
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