UGV-NBWASTE:孟加拉国非生物降解废物的面向数据集
Md Riadul Isalm1, Nabil Bin Mahabub1, Md Jubayar Alam Rafi2
1Department of Computer Science and Engineering, University of Global Village, Barisal 8200, Bangladesh.
Data in brief
|May 9, 2025
概括
一个新的数据集,UGV-NBWASTE,有助于非生物降解废物管理. 它包括在具有挑战性的环境条件下捕获的各种废物类型,以改进检测和分类.
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:
- 非生物降解废物带来了重大的环境挑战,特别是在垃圾填埋场和水生生态系统.
- 有效的管理策略需要准确识别和分类各种废物材料.
- 现有的数据集可能缺乏解决现实世界废物场景所需的多样性.
研究的目的:
- 引入UGV-NBWASTE数据集,用于非生物降解废物管理.
- 提供一系列多样化的废物图像,这些图像是在具有挑战性的环境条件下拍摄的.
- 促进开发可靠的废物检测和分类模型.
主要方法:
- 收集了孟加拉国八种不同类型的非生物降解废物的3600张图像.
- 使用智能手机在各种室内和室外环境中捕捉到的图像,包括水和泥.
- 应用图像预处理技术:质量保证 (验证,清洁) 和增强 (亮度规范化,大小调整).
- 使用面向边界框 (OBB) 进行角度不变检测的注释图像.
主要成果:
- UGV-NBWASTE数据集包括8个常见的,难以管理的非生物降解废物的3600张图像.
- 图像代表着各种各样的条件,包括漂浮在水中和部分埋在泥中.
- 定向的界限框注释允许可靠的废物识别,无论其方向或条件 (平坦,,模糊).
结论:
- UGV-NBWASTE数据集为推进非生物降解废物管理研究提供了宝贵的资源.
- 数据集的多样性和注释格式支持开发更准确和更有弹性的废物检测系统.
- 这种资源可以帮助解决由持久废物造成的环境污染问题.
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