{"title":"WEHD-DETR:一种基于改进RT-DETR的污水管道缺陷实时检测算法","authors":"Guangchao Wei, Zhenzhong Yu, Dongjie Li","doi":"10.1016/j.dsp.2025.105585","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105585"},"PeriodicalIF":3.0000,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WEHD-DETR: A real-time defect detection algorithm for sewer pipelines based on improved RT-DETR\",\"authors\":\"Guangchao Wei, Zhenzhong Yu, Dongjie Li\",\"doi\":\"10.1016/j.dsp.2025.105585\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":51011,\"journal\":{\"name\":\"Digital Signal Processing\",\"volume\":\"168 \",\"pages\":\"Article 105585\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Digital Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1051200425006074\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425006074","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
WEHD-DETR: A real-time defect detection algorithm for sewer pipelines based on improved RT-DETR
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.
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,