视觉污染真实图像对公路的基准数据集
Mohammad AlElaiwi1, Mugahed A Al-Antari2, Hafiz Farooq Ahmad1
1Computer Science Department, College of Computer Sciences and Information Technology (CCSIT), King Faisal University, P.O. Box 400, Al-Ahsa, 31982, Saudi Arabia.
Data in brief
|August 28, 2023
概括
这项研究引入了一个新的数据集,用于检测沙特阿拉伯的视觉污染 (VP),帮助改善城市景观. 该数据集能够自动预测和检测VP元素,如坑洞和障碍物.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 计算机视觉 计算机视觉
背景情况:
- 生活质量 (QoL) 是多方面的,环境质量是关键组成部分.
- 视觉污染 (Visual Pollution,VP) 对城市环境产生负面影响,特别是对公共道路造成影响,例如挖掘障碍,坑洞和破旧人行道等因素.
- 由于其主观性质和缺乏标准化的评估方法,自动检测VP具有挑战性.
研究的目的:
- 开发一个全面的数据集,用于在沙特阿拉伯检测视觉污染.
- 为了促进政府机构对视觉污染的自动预测和检测.
- 通过公开可用的VP图像数据集来增强研究领域.
主要方法:
- 从沙特阿拉伯王国 (KSA) 的各个地区收集了34460张RGB图像的数据集.
- 图像被分为三个类别:挖掘障碍,坑洞和破旧的人行道.
- 深度主动学习 (DAL) 策略用于图像注释,由四位专家进行初始手动注释.
主要成果:
- 数据集包括34460张图像,用于检测和分类任务.
- 该数据集包含8417个用于挖掘障碍物的界限框,25,975个用于坑洞的界限框,以及7412个用于破旧人行道的界限框.
- 现在MOMRAH数据集已经公开了.
结论:
- 开发的MOMRAH数据集为视觉污染检测研究提供了宝贵的资源.
- 使用此数据集可以开发自动化VP检测系统,以改善城市景观.
- 这个倡议支持沙特阿拉伯加强其城市环境的运动.
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