基于深度学习的桥梁甲板路面的损坏检测和定位
Youhao Ni1, Jianxiao Mao1, Yuguang Fu2
1Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究引入了一种使用YOLOv7和修订的LaneNet的新三步方法,用于检测和定位桥甲板路面损坏. 该系统在识别道路损坏和车道线路方面实现了高精度,提高了桥梁维护和安全.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 桥梁甲板路面损坏严重影响驾驶安全和结构寿命.
- 自动检测和定位此类损坏对于有效的桥梁维护至关重要.
研究的目的:
- 提出和评估一个三阶段的方法来检测和定位桥甲板路面损坏.
- 提高自动化桥梁检查系统的准确性和效率.
主要方法:
- 这是一个三阶段的方法,它结合了YOLOv7用于损坏检测和修订的LaneNet用于车道线段.
- 利用2022年道路损坏数据集 (RDD2022) 进行培训和验证.
- 实现图像处理算法用于车道区域提取和损坏定位.
主要成果:
- 在RDD2022数据集上,YOLOv7模型实现了0.663的平均平均精度 (mAP).
- 修订后的LaneNet显示了0.933的车道本地化准确度,超过了实例细分 (0.856).
- 该系统实现了12.3 FPS的推断速度,比实例分割 (6.53 FPS) 快得多.
结论:
- 拟议的方法有效地检测和定位高精度和速度的桥梁甲板路面损坏.
- 这种方法为桥梁甲板路面的维护和检查提供了有价值的参考.
- 整合YOLOv7和修订后的LaneNet为自动化桥梁健康监测提供了一个强大的解决方案.
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