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数据集用于在行人区域检测摩托车手
Nicolás Hernández Díaz1, Yersica C Peñaloza2, Y Yuliana Rios3
1Computer Eng., Universidad Tecnologica de Bolívar, Km 1 vía a Turbaco, Cartagena, 130010, Bolívar, Colombia.
Data in brief
|October 9, 2023
概括
本研究介绍了一种可扩展的基于Python的物联网方法论,通过收集和完善行人和摩托车手的CCTV图像来提高道路安全. 该系统自动获取数据,并包括用户辅助的标签,以创建一个强大的多类对象分类器.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 道路安全是一个关键问题,特别是对于容易受到伤害的道路使用者,如行人和摩托车手.
- 现有的物体检测系统通常需要大型的,标记良好的数据集,这些数据集的创建是耗时且昂贵的.
- 物联网设备和人工视觉的集成为交通分析数据采集提供了一种新的方法.
研究的目的:
- 开发一种半自动化,可扩展的方法来采集和处理使用物联网设备的道路图像.
- 构建一个多类对象分类器,用于识别行人和摩托车手.
- 创建一个高效的数据管道,从公共CCTV料中生成标记数据集.
主要方法:
- 使用Python实现物联网 (IoT) 系统,用于从公共CCTV摄像头连续采集图像.
- 应用人工视觉技术用于自动图像分析和按时间顺序排序数据.
- 开发两种用于数据集调试的算法:一种用于通过感兴趣区域 (ROI) 和热键进行用户辅助标签,另一种用于标签数据的验证.
主要成果:
- 成功获取和处理来自哥伦比亚梅德林的80个CCTV摄像机的异步图像数据.
- 创建一个半自动化管道,用于生成适合训练机器学习模型的调试数据集.
- 展示了一个可扩展的方法来构建用于道路安全应用的多类对象分类器.
结论:
- 拟议的基于物联网的方法提供了一个可扩展和高效的解决方案,用于从真实世界的交通场景中创建对象检测数据集.
- 半自动化方法显著减少了数据标签所需的努力,加速了道路安全AI的发展.
- 这项工作有助于提高道路安全,使得能够构建更准确的多类对象分类器.
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