增强的YOLO v3可在复杂的背景下精确检测桥上的明显损坏
Huifeng Su1, David Bonfils Kamanda2, Tao Han3
1College of Transportation, Shandong University of Science and Technology, Qingdao, 266590, China. skd991970@sdust.edu.cn.
Scientific reports
|April 15, 2024
概括
这项研究增强了YOLO v3算法,用于准确地检测混凝土桥梁疾病,提高了实时结构健康监测的精度和回忆率.
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:
- 混凝土桥梁的恶化对基础设施安全构成重大风险.
- 准确及时检测桥梁疾病对于维护和防止故障至关重要.
- 现有的方法经常与复杂的背景和各种疾病尺度作斗争.
研究的目的:
- 开发一个增强的YOLO v3算法,用于精确识别混凝土桥梁疾病.
- 提高自动桥梁疾病检测系统的准确性和效率.
- 为应对致病密度分布和规模变化带来的挑战.
主要方法:
- 增强的YOLO v3网络,包括Squeeze和Excitation (SE) 网络和空间金字塔聚合.
- 利用CIoU损失功能来提高定位准确度.
- 应用K-means算法用于在1363个桥梁缺陷图像的数据集上对框架进行集群.
- 包括数据增强和手动标签用于数据集改进.
主要成果:
- 增强的YOLO v3模型在精度,回忆和平均精度 (AP) 中显示出显著的改进.
- 与原来的YOLO v3模型相比,平均平均精度 (mAP) 提高了5.5%.
- 在RTX2080Ti显卡上达到每秒84 (FPS) 的检测率,从而实现实时检测.
结论:
- 提议的增强的YOLO v3算法为混凝土桥疾病识别提供了卓越的性能.
- 该方法为更准确,更有效的实时桥梁结构健康监测提供了基础.
- 整合注意力机制和改进的损失功能提高了模型处理复杂的桥面缺陷的能力.
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