RiSID:河流表面图像数据集用于浮动巨型塑料碎片的实例细分
Tomoya Kataoka1,2, Takushi Yoshida3, Natsuki Yamamoto3
1Department of Civil & Environmental Engineering, Ehime University, 3 Bunkyo-cho, Matsuyama, 790-8577, Japan.
Data in brief
|November 10, 2025
概括
研究人员开发了一套新的河流图像数据集,以追踪漂浮的巨型塑料碎片. 这项技术有助于了解从陆地到海洋的塑料运输,这对保护海洋生态系统至关重要.
科学领域:
- 环境科学 环境科学
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 河流是巨型塑料垃圾的重要导管,对海洋生态系统构成威胁.
- 精确量化漂浮的河流巨型塑料对于追踪陆地污染源至关重要.
研究的目的:
- 引入河流表面图像数据集 (RiSID) 用于巨型塑料碎片的监测.
- 促进开发先进的计算机视觉模型来检测和量化漂浮的河流塑料.
主要方法:
- 在日本11个地点收集了7356张高流河面图像.
- 在多个类别中生成了浮动宏观塑料碎片的像素分段注释.
- 格式化注释使用微软在深度学习应用程序上文中的共同对象 (MS COCO) 标准.
主要成果:
- RiSID数据集为漂浮的巨型塑料碎片提供了全面的注释.
- 数据集的结构使得能够探索各种深度学习模型的性能,以检测碎片.
- 这些数据支持对监测河流塑料污染的有效方法的研究.
结论:
- RiSID是推进自动监测河流中浮动巨型塑料垃圾研究的宝贵资源.
- 该数据集将有助于开发更准确的模型来评估塑料运输动态.
- 改进的监测技术对于减轻河流塑料对海洋环境的影响至关重要.
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