一个坑洞视频数据集用于语义细分的语义细分
Muhammad Ihsan1, Muhammad Alfian Amrizal1, Agus Harjoko1
1Department of Computer Science and Electronics, Universitas Gadjah Mada, Indonesia.
Data in brief
|February 16, 2024
概括
一个新的视频数据集有助于洞穴检测. 本资源通过提供用于语义分割算法开发的高分辨率视频来支持道路安全和计算机视觉方面的研究.
科学领域:
- 计算机科学 计算机科学
- 土木工程 土木工程是指土木工程.
- 地理空间分析是什么
背景情况:
- 道路坑道对车辆安全和基础设施完整性构成重大风险.
- 准确的坑洞检测对于道路维护和交通管理至关重要.
- 现有的数据集可能缺乏强大的语义细分模型所需的多样性和分辨率.
研究的目的:
- 引入一套新的高分辨率视频数据集,专门设计用于道路坑洞的语义细分.
- 为了促进先进的坑洞检测算法的开发和基准测试.
- 支持自动驾驶系统和智能交通基础设施的研究.
主要方法:
- 数据集包括619个高分辨率MP4视频,每个视频长度为2秒,共48.
- 视频是在2023年1月在印度尼西亚南加里曼丹的八个村庄拍摄的.
- 数据集被组织成训练,验证和测试集,并有RGB视频和地面真相面具子文件.
主要成果:
- 数据集提供了619个视频与相应的地面真相面具,用于语义细分.
- 它允许从全视频分析到级提取进行灵活的研究.
- 数据支持创建新的注释和自定义数据集配置.
结论:
- 这个视频数据集是计算机视觉和道路安全研究社区的宝贵资源.
- 它可以对语义细分算法进行基准测试,用于洞穴检测.
- 该数据集将推动开发更有效的坑洞分析和缓解策略.
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