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一个使用智能手机的道路缺陷检测系统
1Electronics Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
这项研究介绍了一种基于智能手机的系统,用于自动检测道路缺陷. 它使用深度学习来准确识别坑道和其他道路问题,使基础设施维护更有效率.
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.
- 运输工程 运输工程
背景情况:
- 道路缺陷检测对于基础设施维护和公共安全至关重要.
- 现有的数据收集和分析方法往往是手动的,耗时的和昂贵的.
- 智能手机为数据采集提供了一个无处不在且具有成本效益的平台.
研究的目的:
- 利用智能手机开发一套用于道路缺陷检测的自动化系统.
- 为道路缺陷研究创建一个实用和可靠的数据收集机制.
- 设计和评估一个深度学习模型来分类常见的道路缺陷.
主要方法:
- 开发了一种自动数据收集机制,以收集道路图像和相关数据.
- 基于卷积神经网络 (CNN) 的深度学习模型被设计用于缺陷分类.
- 该模型使用自动收集的数据集进行了训练和验证.
- 性能与传统的道路缺陷检测模型进行了比较.
主要成果:
- 拟议的系统证明了有效的自动数据收集和标签.
- 基于CNN的模型准确地分类了阻速器,人洞和坑洞.
- 开发的模型在检测准确度和处理速度上都超过了传统方法.
- 该系统被证明是可用于商用智能手机的实用性和可扩展性.
结论:
- 这种基于智能手机的新方法为道路缺陷检测提供了实用和高效的解决方案.
- 自动化数据收集和深度学习模型显著推进了道路缺陷研究.
- 这项技术对现实世界的道路基础设施监测和维护应用具有重大前景.
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