在公共道路上使用优化的深卷积神经网络模型自动识别路面裂
Zhihan Lv1, Chen Cheng2, Haibin Lv3
1Department of Game design, Faculty of Arts, 752 36 Uppsala, Uppsala University, Sweden.
概括
本研究引入了一种Mask R-CNN模型,用于有效识别路面困境,在检测各种裂类型时达到高精度. 这种进步有助于改善道路状况和智能城市发展.
科学领域:
- 路面工程工程的路面工程.
- 人工智能的人工智能是人工智能.
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 目前用于路面困境识别的方法通常是低效和具有挑战性的.
- 准确检测路面的困境对于道路维护和安全至关重要.
研究的目的:
- 为了提高自动路面应急识别的效率和准确性.
- 应用深度学习技术来识别道路裂困境.
主要方法:
- 分析路面应急识别方法和类型的分析.
- 描述深度学习概念的路面应急识别.
- 设计和应用Mask R-CNN模型用于道路裂应急识别.
主要成果:
- 面具R-CNN模型实现了全面的识别准确度,范围从95%到99%.
- 特定的裂检测准确度包括:横裂 (95%-98%),纵裂 (92%-98%),以及网状裂 (92%-98%).
结论:
- 开发的Mask R-CNN模型为人行道应急识别提供了一个强大的解决方案.
- 这项研究为在路面分析中应用深层卷积神经网络 (CNN) 提供了参考,并为智慧城市倡议做出了贡献.
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