一个预应力混凝土圆柱管断线检测算法基于改进的YOLOv5
Haoze Li1, Ruizhen Gao2, Fang Sun2
1College of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
一个新的深度学习算法,YOLOv5-Break,使用声学传感有效地识别预应力混凝土圆柱管道 (PCCP) 中的断电线. 这种轻量级模型提供了高精度和降低的计算成本,以改善基础设施监控.
科学领域:
- 土木工程 土木工程是指土木工程.
- 人工智能的人工智能
- 结构健康监测 结构健康监测
背景情况:
- 预压混凝土圆柱管 (PCCP) 的故障对基础设施和经济可行性构成重大风险.
- 对于PCCP来说,传统的检查方法是劳动密集型的,耗时的,并且会破坏运营.
- 开发有效和准确的方法来早期检测PCCP电线断裂对于预防性维护至关重要.
研究的目的:
- 开发一种轻量级的深度学习算法,用于识别PCCP中断电线.
- 提高PCCP电线断裂监控系统的效率和准确性.
- 为了实现实时监控而不需要高性能硬件.
主要方法:
- 建立了一个PCCP断线测试平台,利用分布式光纤声学传感 (DAS) 进行监控.
- 使用连续波形变换 (CWT) 创建了一个模拟断线信号的数据集.
- 提出了一个轻量级的深度学习模型YOLOv5-Break,集成MobileNetV3,Dynamic Conv,协调注意力和Focal_EIoU损失.
主要成果:
- 在定制数据集上,YOLOv5-Break算法实现了97.72%的平均平均精度 (mAP).
- 模型重量减少到7.74 MB,比现有的YOLO版本小得多.
- 与YOLOv8s和YOLOv9s相比,YOLOv5-Break显示出更高的计算效率.
结论:
- 提出的YOLOv5-Break算法有效地识别PCCP断电线的高精度和最小的计算资源.
- 它的轻量化性质和效率使其适合用于实际的PCCP监控系统.
- 这一进步为确保PCCP基础设施的完整性提供了更简单,更具成本效益的解决方案.
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