Abstract:
The escalating crisis of marine plastic pollution, particularly macroplastic debris, poses severe
threats to marine biodiversity, coastal ecosystems, and human livelihoods. Sri Lanka, ranked among
the top global contributors to marine plastic waste, suffers from a lack of systematic and scalable
monitoring mechanism, especially for underwater macroplastic detection. Traditional survey
methods are often manual, labour-intensive, and limited in coverage. Recent advancements in
artificial intelligence, specifically computer vision, present promising alternatives for automating
detection and analysis of marine debris. This review paper critically examines the current landscape
of AI-based approaches for underwater macroplastic detection, highlighting their methodologies,
datasets, performance metrics, and limitations. While global efforts have largely emphasized
surface litter and microplastic detection, underwater macroplastic monitoring remains
underexplored. Moreover, Sri Lanka lacks context-specific studies and affordable, software-based
AI tools that can leverage existing underwater imagery to detect macroplastic waste. By
synthesizing global literature and identifying region-specific research gaps, this paper advocates for
the development of lightweight, AI-powered systems tailored to the Sri Lankan coastline. Such
innovations could enable continuous monitoring, inform policy decisions, and strengthen marine
conservation efforts in data-scarce and resource-constrained settings.
