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Deep learning-based detection of submerged debris and plastics in underwater environments: a systematic review
Original title: Deep learning-based detection of submerged debris and plastics in underwater environments: a systematic review
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This review pulls together 50 studies on AI systems that use underwater cameras to spot plastic debris in oceans and waterways, finding that certain smart, efficient algorithms can now detect trash with over 90% accuracy—even running on small, low-power devices carried by underwater robots. This matters because better tracking of where plastic waste accumulates and breaks down is a key step toward understanding and eventually reducing the microplastics that end up in seafood, drinking water, and our bodies, though the technology still struggles in murky or dark water.
Purpose To synthesize and critically appraise deep learning approaches for detecting submerged plastics and marine debris, with a focus on architectures, image enhancement and deployment on resource-constrained autonomous platforms. Design/methodology/approach A systematic review of 50 peer-reviewed, Scopus-indexed articles (2020–2026) was conducted following PRISMA guidelines, including dual-stage screening, standardized data extraction and qualitative synthesis. Methodological quality was assessed in terms of architectural transparency, dataset reporting, evaluation metrics and comparative baselines. Findings Single-stage detectors (particularly YOLOv11 ensembles) achieve state-of-the-art accuracy [up to 93.9% mean average precision (mAP) @0.5], whereas transformer-based Lightweight Cross-Stage Aggregation Detection Transformer - Pruned version (LCSA-DETR-P) defines the efficiency frontier (105.1 Frames Per Second (FPS) with 31.7% parameter retention). Frequency-domain enhancement (e.g. frequency-domain feature fusion) yields 12–18% mAP gains under high turbidity with modest computational cost, while traditional enhancement offers smaller, context-dependent benefits. Transfer learning and semi/self-supervised strategies can reduce labeled data requirements by approximately 80% and, combined with pruning and quantization, enable real-time inference (30–60 FPS, 85–90% accuracy) on edge devices such as Jetson Nano. Persistent challenges include severe performance degradation in extremely turbid or low-light conditions, limited cross-geographic generalization, and strict onboard power budgets for autonomous underwater vehicles and remotely operated vehicles. Originality/value To the best of the authors’ knowledge, this review is the first to integrate architectural choices, enhancement strategies and optimization/deployment techniques into a unified framework for submerged plastic detection, explicitly linking model design decisions to environmental conditions and edge-computing constraints. It identifies concrete research priorities, including turbidity-resilient multimodal sensing, standardized underwater benchmarks and foundation models tailored to diverse aquatic environments.
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