A Lightweight Coastal Litter Segmentation Network with Feature Gating and Multiscale Context for UAV Imagery

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Litter stranded on beaches can severely degrade local ecosystems and diminish the tourism value of coastal areas. The integration of deep learning into unmanned aerial vehicles (UAVs) provides an effective means for the automatic segmentation of coastal litter. However, large variations in litter scale and complex coastal backgrounds lead to relatively low detection accuracy. In addition, most existing detection models require substantial computational resources, which limits their deployment. To address these issues, we constructed a coastal litter segmentation dataset and developed a lightweight segmentation model, CoastalLitterNet. The dataset contains litter objects of different sizes, colors, and shapes, with various backgrounds. In CoastalLitterNet, a Lightweight Asymmetric U-shaped Backbone Network (L-Net) was designed to reduce model complexity, and Atrous Spatial Pyramid Pooling (ASPP) was introduced to segment litter objects at different scales. A CoastalLitterGate (CLG) was proposed to enhance litter-related features and improve adaptability to complex backgrounds. Comparative experiments showed that the mIoU, IoU1 and F1 of CoastalLitterNet reached 93.95%, 88.42% and 93.85%, respectively. The Params and FLOPs of the model are only 12.90 M and 189.30 G, respectively. The quantitative and qualitative results show that CoastalLitterNet effectively segments litter objects at varying scales and adapts well to complex coastal environments while maintaining a lightweight architecture.