通过红外热成像检测外部后张法管道灌浆缺陷的实时轻量级 YOLO 模型

IF 9.6 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY
Shengli Li , Shiji Sun , Yang Liu , Wanshuai Qi , Nan Jiang , Can Cui , Pengfei Zheng
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引用次数: 0

摘要

利用红外热成像技术自动分析外部后张法筋管灌浆缺陷,区分缺陷区域是一项挑战。为了实现高效稳定的自动检测,本文提出了一种基于 YOLO 深度学习的轻量级灌浆缺陷实时检测方法。首先,使用 Cutpaste 数据增强方法有效缓解了过拟合问题。然后,在颈部网络中引入 C3Ghost 模块,并将网络层的通道数调整为 YOLOv5s 模型的 50%,减少了参数数量和计算资源。最后,利用 SGD 优化器和 GIOU 损失函数以及 Sim attention 模块提高了检测精度。根据实例分析和比较,该方法的[email protected] 达到 96.9 %,检测速度达到 66FPS。与 YOLOv5s 相比,参数数量减少了 79%,FLOPs 减少了 77%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time lightweight YOLO model for grouting defect detection in external post-tensioned ducts via infrared thermography
It is challenging to distinguish the defective areas using infrared thermography to automatically analyze external post-tensioned tendon duct grouting defects. To achieve efficient and stable automated detection, a lightweight real-time grouting defects detection method based on YOLO deep learning is proposed. Firstly, the Cutpaste data augmentation method was used to effectively alleviate the problem of overfitting. Then, the C3Ghost module was introduced into the neck network, and the number of channels in the network layers was adjusted to 50 % of those in the YOLOv5s model, reducing the number of parameters and computational resources. Finally, the SGD optimizer and GIOU loss function, as well as the Sim attention module, were used to improve detection accuracy. Based on instance analysis and comparison, this method achieves [email protected] of 96.9 % and detection speed of 66FPS. Compared with YOLOv5s, it reduces the number of parameters by 79 % and FLOPs by 77 %.
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来源期刊
Automation in Construction
Automation in Construction 工程技术-工程:土木
CiteScore
19.20
自引率
16.50%
发文量
563
审稿时长
8.5 months
期刊介绍: Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities. The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.
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