WEHD-DETR: A real-time defect detection algorithm for sewer pipelines based on improved RT-DETR

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Guangchao Wei, Zhenzhong Yu, Dongjie Li
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引用次数: 0

Abstract

Drainage pipe defects impose numerous negative impacts on society, the environment, and public safety. Excessive accumulation of sediment and obstructions in pipelines significantly reduces their water flow capacity, rendering urban areas highly susceptible to flooding during heavy rainfall and posing serious safety hazards. Additionally, structural defects such as misaligned joints and cracks can lead to groundwater leakage, potentially triggering geological disasters, including road collapses. Therefore, regular inspection of drainage pipelines is essential to ensure their proper functioning and to support urban safety and sustainable development. However, the accuracy and efficiency of current pipeline defect detection methods remain limited due to factors such as poor-quality early-stage images, insufficient data samples, complex internal pipeline backgrounds, and suboptimal lighting conditions. To address these issues, this study proposes a real-time pipeline defect detection method based on an improved RT-DETR algorithm. The method incorporates a lightweight backbone network and integrates an enhanced adaptive feature fusion module, dilated convolution, and structural reparameterization techniques, thereby improving the model's ability to extract and fuse pipeline defect information. Experimental results demonstrate that this method achieves efficient and accurate identification of five common types of pipeline defects. Compared to the original RT-DETR, the mean average precision (mAP) increases by 3.1%, the detection speed reaches 75.2 frames per second, and the model parameters are reduced by 34.6%. While maintaining high detection accuracy, the method significantly enhances detection efficiency and reduces computational resource consumption, making it suitable for real-time pipeline defect detection in complex environments.
WEHD-DETR:一种基于改进RT-DETR的污水管道缺陷实时检测算法
排水管缺陷对社会、环境和公共安全造成了诸多负面影响。泥沙淤积和管道堵塞严重降低了管道的通水能力,使城市地区在强降雨时极易发生洪涝灾害,存在严重的安全隐患。此外,结构缺陷,如错位的接缝和裂缝可能导致地下水泄漏,潜在地引发地质灾害,包括道路塌陷。因此,定期检查排水管道对于确保其正常运作和支持城市安全和可持续发展至关重要。然而,由于早期图像质量差、数据样本不足、管道内部背景复杂、光照条件不理想等因素,现有管道缺陷检测方法的准确性和效率仍然有限。针对这些问题,本研究提出了一种基于改进RT-DETR算法的管道缺陷实时检测方法。该方法采用了轻量级骨干网络,并集成了增强的自适应特征融合模块、扩展卷积和结构重参数化技术,从而提高了模型提取和融合管道缺陷信息的能力。实验结果表明,该方法能够有效、准确地识别五种常见的管道缺陷。与原始RT-DETR相比,平均精度(mAP)提高3.1%,检测速度达到75.2帧/秒,模型参数降低34.6%。该方法在保持较高检测精度的同时,显著提高了检测效率,减少了计算资源消耗,适用于复杂环境下的管道缺陷实时检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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