更新"BePLi数据集v1:海塑料垃圾数据集版本1,例如海塑料垃圾的细分"以13个对象类
Mitsuko Hidaka1,2, Koshiro Murakami2, Shintaro Kawahara2
1Graduate School of Science and Engineering, Department of Engineering, Ocean Civil Engineering Program, Kagoshima University, 1-21-40 Korimoto, Kagoshima-shi, Kagoshima, 890-0065, Japan.
Data in brief
|August 5, 2025
概括
在降解之前,监测海塑料垃圾至关重要. BePLi数据集v2提供了详细的注释,用于使用深度学习自动检测和分析巨型塑料污染.
科学领域:
- 环境科学 环境科学
- 计算机视觉 计算机视觉
- 海洋生物学 海洋生物学
背景情况:
- 海积累了大量的塑料垃圾,这些塑料碎片成微塑料,带来了环境挑战.
- 有效监测海上的巨型塑料垃圾对于防止进一步的退化和环境损害至关重要.
- 使用遥感数据进行详细的垃圾分布分析需要自动化,客观的图像处理方法.
研究的目的:
- 引入BePLi数据集v2,这是一个更新的资源,用于训练海塑料垃圾检测的深度学习模型.
- 为开发自动化图像分析技术提供具有详细注释的全面数据集.
- 促进沙垃圾监测和管理方面的研究.
主要方法:
- BePLi数据集v2包含来自日本西北海岸的3722张原始图像.
- 它包括在像素和单个对象层面上对塑料垃圾的118,572个手动注释.
- 物品被分为13个不同的塑料类别,包括"物瓶"",塑料袋"和"渔网".
主要成果:
- 该数据集支持对宏观沙塑料垃圾的实例细分和物体检测模型的开发.
- 标注包括边界框和像素级别的面具,允许各种分析级别.
- 该资源有助于计数物体和估计海上的垃圾覆盖面.
结论:
- BePLi数据集v2是推动自动化海塑料垃圾检测的宝贵资源.
- 它可以开发用于环境监测的复杂深度学习模型.
- 该数据集支持海洋污染研究和管理中的多种应用.
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