A Prestressed Concrete Cylinder Pipe Broken Wire Detection Algorithm Based on Improved YOLOv5.

IF 3.4 3区 综合性期刊 Q2 CHEMISTRY, ANALYTICAL Sensors Pub Date : 2025-02-06 DOI:10.3390/s25030977
Haoze Li, Ruizhen Gao, Fang Sun, Yv Wang, Baolong Ma
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Abstract

The failure accidents of prestressed concrete cylinder pipe (PCCP) seriously affect the economic feasibility of the construction site. The traditional method of needing to stop construction for pipe inspection is time-consuming and laborious. This paper studies the PCCP broken wire identification algorithm based on deep learning. A PCCP wire-breaking test platform was built; the Distributed Fiber Acoustic Sensing Monitoring System (DAS) monitors wire-breakage events in DN4000mm PCCPs buried underground. The collected broken wire signal creates a time-frequency spectrum diagram dataset of the simulated broken wire signal through continuous wavelet transform (CWT). Considering the location of equipment limitations, based on the YOLOv5 algorithm, a lightweight algorithm, YOLOv5-Break is proposed for broken wire monitoring. Firstly, MobileNetV3 is used to replace the YOLOv5 network backbone, and Dynamic Conv is used to replace Conv in C3 to reduce redundant computation and memory access; the coordinate attention mechanism is integrated into the C3 module to make the algorithm pay more attention to location information; at the same time, CIOU is replaced by Focal_EIoU to make the algorithm pay more attention to high-quality samples and balance the uneven problem of complex and easy examples. The YOLOv5-Break algorithm achieves a mAP of 97.72% on the self-built broken wire dataset, outperforming YOLOv8, YOLOv9, and YOLOv10. Notably, YOLOv5-Break reduces the model weight to 7.74 MB, 46.25% smaller than YOLOv5 and significantly lighter than YOLOv8s and YOLOv9s. With a computational cost of 8.3 GFLOPs, YOLOv5-Break is 71.0% and 78.5% more efficient than YOLOv8s and YOLOv9s. It can be seen that the lightweight algorithm YOLOv5-Break proposed in this article simplifies the algorithm without losing accuracy. Moreover, the lightweight algorithm does not require high hardware computing power and can be better arranged in the PCCP broken wire monitoring system.

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预应力混凝土圆筒管(PCCP)的故障事故严重影响了施工现场的经济可行性。传统的管道检测方法需要停止施工,费时费力。本文研究了基于深度学习的 PCCP 断丝识别算法。建立了一个 PCCP 断线测试平台;分布式光纤声学传感监测系统(DAS)监测埋在地下的 DN4000mm PCCP 的断线事件。采集到的断线信号通过连续小波变换(CWT)生成模拟断线信号的时频谱图数据集。考虑到设备位置的限制,在 YOLOv5 算法的基础上,提出了一种用于断线监测的轻量级算法 YOLOv5-Break。首先,用 MobileNetV3 代替 YOLOv5 网络骨干,用 Dynamic Conv 代替 C3 中的 Conv,以减少冗余计算和内存访问;在 C3 模块中集成坐标关注机制,使算法更加关注位置信息;同时,用 Focal_EIoU 代替 CIOU,使算法更加关注高质量样本,平衡复杂例子和简单例子的不均衡问题。YOLOv5-Break 算法在自建的断线数据集上实现了 97.72% 的 mAP,优于 YOLOv8、YOLOv9 和 YOLOv10。 值得注意的是,YOLOv5-Break 将模型权重减少到 7.74 MB,比 YOLOv5 小 46.25%,比 YOLOv8s 和 YOLOv9s 轻很多。计算成本为 8.3 GFLOPs,YOLOv5-Break 比 YOLOv8s 和 YOLOv9s 的效率分别高出 71.0% 和 78.5%。由此可见,本文提出的轻量级算法 YOLOv5-Break 在简化算法的同时不失准确性。此外,轻量级算法对硬件计算能力要求不高,可以更好地布置在 PCCP 断线监测系统中。
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来源期刊
Sensors
Sensors 工程技术-电化学
CiteScore
7.30
自引率
12.80%
发文量
8430
审稿时长
1.7 months
期刊介绍: Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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