超越传统监测:一个语义细分方法来量化未密封道路上交通诱导的灰尘
Asanka de Silva1, Rajitha Ranasinghe1, Arooran Sounthararajah1
1ARC Industrial Transformation Research Hub (ITRH)-SPARC Hub, Department of Civil Engineering, Monash University, Clayton Campus, Clayton, VIC 3800, Australia.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
这项研究引入了一种新的深度学习方法,语义细分,以量化交通引起的道路灰尘. 这种方法为昂贵的传统尘埃监测方法提供了务实而强大的替代方案.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
背景情况:
- 道路尘埃带来了严重的健康风险和环境问题.
- 目前的尘埃监测方法昂贵,需要专门的专业知识.
- 交通引起的尘埃排放影响道路基础设施和用户安全.
研究的目的:
- 开发一种新,务实的,可靠的方法来量化交通引起的道路灰尘.
- 为了利用深度学习,特别是语义细分,用于像素智能的尘埃识别.
- 为了验证拟议的方法对现实尘埃测量的有效性.
主要方法:
- 使用预先选择的高性能语义细分机器学习模型.
- 应用模型用于像素识别图像中的道路灰尘.
- 确定尘埃像素的数量与研究级尘埃监测器的数据相关联.
主要成果:
- 证明语义细分可以合理量化交通引起的尘埃.
- 在预测低尘埃度 (真正正象限) 方面取得了超过90%的准确性.
- 验证了实时应用的方法,并公开提供了代码.
结论:
- 语义细分为道路灰尘量化提供了一种可行且准确的方法.
- 开发的方法为传统监测技术提供了具有成本效益和可访问性的替代方案.
- 该研究为实时道路灰尘监测和管理系统提供了基础.
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