基于深度学习的城市道路基层缺陷智能识别研究
Yanli Qi1, Mingzhou Bai2,3, Zelin Li4
1School of Civil Engineering, Beijing Jiaotong University, Beijing, 100044, China.
Scientific reports
|November 15, 2024
概括
这项研究使用地质雷达和深度学习来识别城市道路下层层缺陷. faster_rcnn_inception_v2算法显示出对道路基础设施的智能,非破坏性测试的承诺.
科学领域:
- 地质技术工程 地质技术工程
- 土木工程 土木工程是指土木工程.
- 人工智能的人工智能
背景情况:
- 城市道路次级由于多样化的类型而面临越来越多的缺陷和安全风险.
- 非破坏性测试 (NDT) 对于评估底层健康状况和预防事故至关重要.
研究的目的:
- 开发一种智能系统,使用地质雷达识别城市道路的地下污染缺陷.
- 评估深度学习算法的有效性,以分析地质雷达数据.
主要方法:
- 使用了GprMax软件,用于对具有缺陷的多层子级模型进行前向模拟.
- 使用模拟和现场数据创建了一个地质雷达次级缺陷图像数据库.
- 应用并比较了四个改进的Faster R-CNN深度学习算法,用于缺陷检测和分类.
主要成果:
- 更快的_rcnn_inception_v2算法在识别次级缺陷方面表现出卓越的性能.
- 关键指标如损失值,区域识别和准确性被用于算法比较.
- 建立了一个强大的数据库来培训和验证深度学习模型.
结论:
- 使用地质雷达和深度学习,可以实现城市道路地下层缺陷的智能识别.
- 更快的_rcnn_inception_v2模型非常适合用于道路次级的NDT.
- 这种方法通过先进的诊断来提高道路安全和基础设施管理.
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