This research presents the condition prediction of sewer pipes using a linear regression approach. The analysis is based on data obtained via Closed Circuit Television (CCTV) inspection over a sewer system. Informatio...This research presents the condition prediction of sewer pipes using a linear regression approach. The analysis is based on data obtained via Closed Circuit Television (CCTV) inspection over a sewer system. Information such as pipe material and pipe age is collected. The regression approach is developed to evaluate factors which are important and predict the condition using available information. The analysis reveals that the method can be successfully used to predict pipe condition. The specific model obtained can be used to assess the pipes for the given sewer system. For other sewer systems, the method can be directly applied to predict the condition. The results from this research are able to assist municipalities to forecast the condition of sewer pipe mains in an effort to schedule inspection, allocate budget and make decisions.展开更多
Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential malfunctioning.In recent years,to relieve the manual efforts by human experts,models based ...Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential malfunctioning.In recent years,to relieve the manual efforts by human experts,models based on deep learning have been introduced to automatically identify potential defects.However,these models are insufficient in terms of dataset complexity,model versatility and performance.Our work addresses these issues with amulti-stage defect detection architecture using a composite backbone Swin Transformer.Themodel based on this architecture is trained using a more comprehensive dataset containingmore classes of defects.By ablation studies on the modules of combined backbone Swin Transformer,multi-stage detector,test-time data augmentation and model fusion,it is revealed that they all contribute to the improvement of detection accuracy from different aspects.The model incorporating all these modules achieves the mean Average Precision(mAP)of 78.6% at an Intersection over Union(IoU)threshold of 0.5.This represents an improvement of 14.1% over the ResNet50 Faster Region-based Convolutional Neural Network(R-CNN)model and a 6.7% improvement over You Only Look Once version 6(YOLOv6)-large,the highest in the YOLO methods.In addition,for other defect detection models for sewer pipes,although direct comparison with themis infeasible due to the unavailability of their private datasets,our results are obtained from a more comprehensive dataset and have superior generalization capabilities.展开更多
油脂沉积物(fat,oil and grease deposits,FOGDs)是家庭、餐饮和工业废水中的脂肪、油和油脂等物质在污水管道输送过程中反应、与水中悬浮颗粒(SS)聚集并最终沉积下来的块状固体物。一方面阻碍污水在下水道系统中的顺利流动,引发城市内...油脂沉积物(fat,oil and grease deposits,FOGDs)是家庭、餐饮和工业废水中的脂肪、油和油脂等物质在污水管道输送过程中反应、与水中悬浮颗粒(SS)聚集并最终沉积下来的块状固体物。一方面阻碍污水在下水道系统中的顺利流动,引发城市内涝和环境污染;另一方面造成管道内微生物的大量繁殖,产生甲烷(CH_(4))和硫化氢(H_(2)S)等温室气体增加管道内的碳排放,引发管道气体爆炸等安全风险。本研究从排水管道中FOGDs的危害出发,采用文献计量学方法,围绕污水管道中物理、化学、生物等不同类型要素对FOGDs形成和特性的影响进行综述,进而结合废水中脂肪、油和油脂等物质在管道中的水流输运、化学反应、微生物附着与降解过程解析,进一步探讨污水管道中FOGDs形成的物理、化学和生物机制,并提出该领域未来的研究方向,为城镇污水排水系统的减污降碳和提质增效提供支撑。展开更多
文摘This research presents the condition prediction of sewer pipes using a linear regression approach. The analysis is based on data obtained via Closed Circuit Television (CCTV) inspection over a sewer system. Information such as pipe material and pipe age is collected. The regression approach is developed to evaluate factors which are important and predict the condition using available information. The analysis reveals that the method can be successfully used to predict pipe condition. The specific model obtained can be used to assess the pipes for the given sewer system. For other sewer systems, the method can be directly applied to predict the condition. The results from this research are able to assist municipalities to forecast the condition of sewer pipe mains in an effort to schedule inspection, allocate budget and make decisions.
基金supported by the Science and Technology Development Fund of Macao(Grant No.0079/2019/AMJ)the National Key R&D Program of China(No.2019YFE0111400).
文摘Urban sewer pipes are a vital infrastructure in modern cities,and their defects must be detected in time to prevent potential malfunctioning.In recent years,to relieve the manual efforts by human experts,models based on deep learning have been introduced to automatically identify potential defects.However,these models are insufficient in terms of dataset complexity,model versatility and performance.Our work addresses these issues with amulti-stage defect detection architecture using a composite backbone Swin Transformer.Themodel based on this architecture is trained using a more comprehensive dataset containingmore classes of defects.By ablation studies on the modules of combined backbone Swin Transformer,multi-stage detector,test-time data augmentation and model fusion,it is revealed that they all contribute to the improvement of detection accuracy from different aspects.The model incorporating all these modules achieves the mean Average Precision(mAP)of 78.6% at an Intersection over Union(IoU)threshold of 0.5.This represents an improvement of 14.1% over the ResNet50 Faster Region-based Convolutional Neural Network(R-CNN)model and a 6.7% improvement over You Only Look Once version 6(YOLOv6)-large,the highest in the YOLO methods.In addition,for other defect detection models for sewer pipes,although direct comparison with themis infeasible due to the unavailability of their private datasets,our results are obtained from a more comprehensive dataset and have superior generalization capabilities.
文摘油脂沉积物(fat,oil and grease deposits,FOGDs)是家庭、餐饮和工业废水中的脂肪、油和油脂等物质在污水管道输送过程中反应、与水中悬浮颗粒(SS)聚集并最终沉积下来的块状固体物。一方面阻碍污水在下水道系统中的顺利流动,引发城市内涝和环境污染;另一方面造成管道内微生物的大量繁殖,产生甲烷(CH_(4))和硫化氢(H_(2)S)等温室气体增加管道内的碳排放,引发管道气体爆炸等安全风险。本研究从排水管道中FOGDs的危害出发,采用文献计量学方法,围绕污水管道中物理、化学、生物等不同类型要素对FOGDs形成和特性的影响进行综述,进而结合废水中脂肪、油和油脂等物质在管道中的水流输运、化学反应、微生物附着与降解过程解析,进一步探讨污水管道中FOGDs形成的物理、化学和生物机制,并提出该领域未来的研究方向,为城镇污水排水系统的减污降碳和提质增效提供支撑。
