Accurate quantification of the physical exposure area of beach litter, rather than simple item counts, is essential for credible ecological risk assessment of marine debris. However, automated UAV-based monitoring predominantly relies on bounding-box detection, which systematically overestimates the planar area of irregular litter objects. To address this geometric limitation, we develop PLAS-Net (Pixel-level Litter Area Segmentor), an instance segmentation framework that extracts pixel-accurate physical footprints of coastal debris. Evaluated on UAV imagery from a monsoon-driven pocket beach in Koh Tao, Thailand, PLAS-Net achieves a mAP_50 of 58.7% with higher precision than eleven baseline models, demonstrating improved mask fidelity under complex coastal conditions. To illustrate how the accuracy of the masking affects the conclusions of environmental analysis, we conducted three downstream demonstrations: (i) power-law fitting of normalized plastic density (NPD) to characterize fragmentation dynamics; (ii) area-weighted ecological risk index (ERI) to map spatial pollution hotspots; and (iii) source composition analysis revealing the abundance-area paradox: fishing gear constitutes a small proportion of the total number of items, but has the largest physical area per unit item. Pixel-level area extraction can provide more valuable information for coastal monitoring compared to methods based solely on counting.

翻译：相比于简单的垃圾数量统计，准确量化海滩垃圾的物理暴露面积对于海洋垃圾的生态风险评估至关重要。然而，基于无人机的自动化监测主要依赖于边界框检测，这会系统性地高估不规则垃圾物体的平面面积。为解决这一几何限制，我们开发了PLAS-Net（像素级垃圾区域分割器），一种从海岸垃圾中提取像素级精确物理足迹的实例分割框架。在泰国涛岛季风驱动型袋状海滩的无人机影像上评估，PLAS-Net的mAP_50达到58.7%，且精度高于十一个基线模型，展示了在复杂海岸条件下改进的掩膜保真度。为说明掩膜精度如何影响环境分析的结论，我们进行了三项下游演示：（i）归一化塑料密度（NPD）的幂律拟合以表征碎裂动力学；（ii）面积加权生态风险指数（ERI）以绘制空间污染热点图；（iii）来源组成分析揭示了数量-面积悖论：渔具在垃圾总数中占比很小，但单位物品的物理面积最大。与仅基于计数的方法相比，像素级面积提取可为海岸监测提供更有价值的信息。