Abstract
Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.
Funder
FCT
Environment Research and Technology Development Fund
Environmental Restoration and Conservation Agency of Japan
national funds through FCT—Fundação para a Ciência e a Tecnologia
Scientific Employment Stimulus—Institutional Calls
FCT/FCUL
CleanAtlantic
INTERREG Atlantic Area Program
Oceanlit
INTERTAGUA
IN-TERREG- MAC
LARGESCALE
FCT—Fundação Para a Ciência e Tecnologia
New Energy and Industrial Technology Development Organization
Subject
General Earth and Planetary Sciences
Cited by
4 articles.
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