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Shoreline-Adaptive Plastic Segmentation Network (SAPS-Net): A Dual-Cascade Framework with Cross-Modulated Feature Fusion for Automated Coastal Litter Monitoring
Summary
Researchers built an AI tool that can automatically spot plastic trash on beaches—from big tangled nets down to tiny fragments—by first recognizing what kind of shoreline it's looking at (sandy, rocky, etc.) and then using a specialized detection method for that setting. This matters because catching small plastic pieces early, before they break down further into microplastics that can enter our food and water supply, makes cleanup efforts faster and more targeted, and the tool proved highly accurate in real-world testing on an actual coastline.
Automated monitoring of coastal plastic pollution is critical for environmental management, but is severely hindered by the complexity of shoreline environments. Generic computer vision models fail due to extreme scale variation (from large nets to small fragments), complex background clutter (e.g., seaweed, rocks), and object deformation. We introduce the Shoreline-Adaptive Plastic Segmentation Network (SAPS-Net), a novel dual-cascade framework that addresses these “in-the-wild” challenges. SAPS-Net first classifies the shoreline’s environmental context (e.g., sandy, rocky) to dynamically load a specialized “expert” segmentation model. The model integrates bespoke modules for scale-adaptive attention and noise-gated dynamic convolution to precisely segment both large debris and small fragments. Trained on our new, large-scale Coastal Plastic Litter Data set (CPLD), SAPS-Net achieves a state-of-the-art 70.1% Average Precision (AP), significantly outperforming baselines (e.g., +17.6 AP vs Mask R-CNN). Crucially, it demonstrates a 10.0-point improvement in small-object AP (APs), addressing the most prevalent form of pollution. Empirical validation in a real-world field study (Hebei Province) confirmed the model’s robustness, achieving 0.90 F1-score and 0.95 precision. SAPS-Net provides a validated, scalable, and efficient tool for high-resolution litter mapping, optimizing cleanup logistics, and informing environmental policy.