珠海市某区域污水管网CCTV检测结果与建议

为满足城市规划、建设和后续管养维护工作需要,珠海市开展了全市污水管网普查工作。对某区域的污水管网系统闭路电视(CCTV)的检测结果进行统计分析,显示管网系统存在较大缺陷,其中管道结构性缺陷有898处,主要表现为管道错口、脱节和渗漏,三类缺陷累积所占缺陷比例为85.08%;管道功能性缺陷有283处,主要表现为管道沉积、障碍物和结垢,三类缺陷累积所占缺陷比例为91.52%。进一步分析缺陷类型与对应等级,其管道脱节缺陷和沉积缺陷最为严重,管网系统有结构破坏和运行瘫痪的风险,建议及时开展管网修复工作,确保污水系统的安全运行。根据管道CCTV检测统计结果及分析,建议针对珠海淤泥软土层地质情况,加强管道接口及基础处理,可减轻与延缓管道结构性缺陷;同时加强管网养护力度,减少管道功能性缺陷。     

CCTV 技术在泗洪县城区排水管道检测中的应用

CCTV结合机器人的管道检测技术是一种智能化检测手段。本文利用该技术对泗洪县城区管道进行检测。结果表明,管道结构性缺陷在数量和严重程度上远超功能性缺陷;管道结构性缺陷主要是腐蚀、脱节、破裂,功能性缺陷则集中在树根和结垢。     

CCTV管道内窥技术在城市排水管道检测中的应用

本文介绍了CCTV管道内窥检测技术的工作原理、工作程序,详细论述了检测影像判读及管道评估计算方法,并以某城市排水管道CCTV检测为例介绍了CCTV管道内窥检测技术的应用及效果。     

排水管道闭路电视视频智能检测技术的现状与挑战

城市排水管道缺陷检测是及时发现管道安全隐患的重要保障,为管道养护、修复提供准确的科学依据.人工管道缺陷检测方法费时费力,主观误差大,与其相比,管道闭路电视检测系统(Closed Circuit Television,CCTV)管道内窥检测方法自动化程度更高,但仍处于发展的初级阶段.本文首先分析人工检测方法与传统机器学习...     

CCTV用于成都市锦兴路排水管道检测与评估

以成都市锦兴路排水管道非开挖检测工程为例,介绍了闭路电视(CCTV)检测技术在城市污水管道功能检测评估中的应用,包括管道预处理、水位判断以及CCTV检测。根据CCTV检测结果,该段排水管道存在39处缺陷,主要为腐蚀和沉积,且腐蚀管道为整段腐蚀,总长度达到890 m。结合国家行业标准对该排水管道进行了结构性和功能性评估。结构性评估结果表明,该段管道修复等级为二级,管道在短期内不会发生破坏,但应做修复计划;功能性评估结果表明,管道养护等级为四级,输水功能受到严重影响,应立即对淤积的管道进行处理。     

市政管网缺陷分布图快速绘制方法

近年来,利用排水管道内窥摄像CCTV检测系统、QV管道潜望镜设备检测管网内部结构性缺陷及功能性缺陷越来越普遍。市政管网内窥检测后,需用不同的颜色、符号、图层在已有的管网CAD分布图中标注出管道缺陷。本文介绍利用Visual Lisp语言编制程序,实现管网缺陷标注自动绘制,提高工作效率。     

CCTV和探地雷达技术在城市道路病害中的应用

与传统的排水管道检测相比,CCTV管道检测技术有着图像清晰直观、可探管径范围大、安全性能高等优势。可以清晰的判断环向和管道延伸方向缺陷的位置,但是在管道外壁却不能准确的判断缺陷的形态、大小、延伸长度等病害状况。两种方法相结合可以更好地为城市道路病害的预警、修复等工作提供依据。     

某压力污水箱涵结构检测及安全性分析实例

污水箱涵是城市排水体系的重要基础设施。箱涵一般由钢筋混凝土材料制成,在长期运营过程中,容易出现混凝土腐蚀、钢筋锈蚀等结构缺陷,影响结构安全性。目前,普通管道检测侧重于通过管道内部CCTV检测了解结构腐蚀,对不同运营工况下的承载力考虑不足,且对压力箱涵打开检查井会导致污水外溢,故无法从内部开展检测。以上海市合流污水治理一期工程为背景,通过开挖样孔对箱涵结构从外部进行安全性检测,即通过开挖样孔暴露检测面,综合利用三维超声、雷达、冲击回波判断顶板腐蚀程度,结合回弹法测强、钢筋探测、管段变形缝处错台测量,了解材料强度和变形。基于检测参数,建立箱涵结构有限元模型,分析5种典型运营工况下的承载力,从而为箱涵的维修加固提供依据,解决了压力管涵无法按照CCTV规范从内部进行检测评估的问题,弥补了现行规范的不足。     

CCTV技术在排水管道状态检测中的应用

介绍了CCTV管道内窥技术的工作原理、程序,详细阐述了影像判读及管道状态评估的计算方法和步骤,并以某排水管道CCTV检测工程为例介绍了该技术在排水管道状态检测中的应用情况和效果。     

节点流量衡算法预测污水处理厂外来水量

基于关键节点水量调查,对天津某污水处理厂服务范围内外来水进行调查和分析,初步确定了外来水水量和可能的入侵管段,为下一步通过管道检测确定管道缺陷位置提供依据。     

Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques

Sanitary sewer systems are designed to collect and transport sanitary wastewater and stormwater. Pipe inspection is important in identifying both the type and location of pipe defects to maintain the normal sewer operations. Closed-circuit television (CCTV) has been commonly utilized for sewer pipe inspection. Currently, interpretation of the CCTV images is mostly conducted manually to identify the defect type and location, which is time-consuming, labor-intensive and inaccurate. Conventional computer vision techniques are explored for automated interpretation of CCTV images, but such process requires large amount of image pre-processing and the design of complex feature extractor for certain cases. In this study, an automated approach is developed for detecting sewer pipe defects based on a deep learning technique namely faster region-based convolutional neural network (faster R-CNN). The detection model is trained using 3000 images collected from CCTV inspection videos of sewer pipes. After training, the model is evaluated in terms of detection accuracy and computation cost using mean average precision (mAP), missing rate, detection speed and training time. The proposed approach is demonstrated to be applicable for detecting sewer pipe defects accurately with high accuracy and fast speed. In addition, a new model is constructed and several hyper-parameters are adjusted to study the influential factors of the proposed approach. The experiment results demonstrate that dataset size, initialization network type and training mode, and network hyper-parameters have influence on model performance. Specifically, the increase of dataset size and convolutional layers can improve the model accuracy. The adjustment of hyper-parameters such as filter dimensions or stride values contributes to higher detection accuracy, achieving an mAP of 83%. The study lays the foundation for applying deep learning techniques in sewer pipe defect detection as well as addressing similar issues for construction and facility management.     

Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine

Condition assessment of municipal sewer pipes using closed circuit television (CCTV) inspections is known to be time consuming, costly, and prone to errors primarily due to operator fatigue or novicity. Automated detection of defects can provide a valuable tool for ensuring the quality, accuracy, and consistency of condition data, while reducing the time and cost of the inspection process. This paper presents an efficient pattern recognition algorithm to support automated detection and classification of pipe defects in images obtained from conventional CCTV inspection videos. The algorithm employs the histograms of oriented gradients (HOG) and support vector machine (SVM) to identify pipe defects. The algorithm involves two main steps: (1) image segmentation to extract suspicious regions of interest (ROI) that represent candidate defect areas; and (2) classification of the ROI using SVM classifier that was trained using sets of HOG features extracted from positive and negative examples of the defect. Proposed algorithm is applied to the problem of detecting tree root intrusions. The performance of linear and radial basis function SVM classifiers evaluated. The algorithm was tested on a set of actual CCTV videos obtained from the cities of Regina and Calgary in Canada. Experimental results demonstrated the viability and robustness of the algorithm.     

Sewer pipe defects diagnosis assessment using multivariate analysis on CCTV video imagery

Closed circuit television (CCTV) technology has been commonly used to inspect underground pipe defects, and high CCTV image quality is a prerequisite for accurate defect diagnosis. An acceptance criterion for CCTV inspection videos is critical for ensuring accurate diagnosis and preventing disputes between employers and contractors. This paper used multivariate statistical methods to evaluate the overall quality of CCTV images and to define an acceptance criterion for CCTV videos. Numerous CCTV images from a sewer inspection project were assessed and their quality, consisting of similarity in luminance and contrast distortions, was calculated by comparing a set of ideal images. Principal component analysis (PCA) and redundancy analysis (RDA) grouped the CCTV videos into homogeneous segments with similar image quality and provided a visual acceptance criterion for CCTV inspection videos. Furthermore, RDA triplot indicated that the contrast improvement of CCTV images can effectively enhance image quality and increase the diagnosis efficiency.     

Optical flow techniques for estimation of camera motion parameters in sewer closed circuit television inspection videos

This paper discusses a novel approach for automated analysis and tracking of camera motion in sewer inspection closed circuit television (CCTV) videos. This approach represents an important building block for any system that supports automated analysis and defect detection of CCTV videos. The proposed approach employs optical flow techniques to automatically identify, locate, and extract a limited set of video segments, called regions of interest (ROI), which likely include defects, thus reducing the time and computational requirements needed for video processing. Tracking the camera motion parameters is used to recover the operator actions during the inspection session, which would provide important clues about the location and severity of the ROI. Techniques for estimating the camera travelling distance, position inside the sewer, and direction of motion from optical flow vectors are discussed. The proposed techniques were validated using a representative set of sewer CCTV videos obtained from the cities of Regina and Calgary, Canada.     

A unified convolutional neural network integrated with conditional random field for pipe defect segmentation

Semantic segmentation of closed‐circuit television (CCTV) images can facilitate automatic severity assessment of sewer pipe defects by assigning defect labels to each pixel on the image, from which defect types, locations, and geometric information can be obtained. In this study, a unified neural network, namely DilaSeg‐CRF, is proposed by fully integrating a deep convolutional neural network (CNN) with dense conditional random field (CRF) for improving the segmentation accuracy. First, DilaSeg is constructed with dilated convolution and multiscale techniques for producing feature maps with high resolution. The steps of the dense CRF inference algorithm are converted into CNN operations, which are then formulated as recurrent neural network (RNN) layers. The DilaSeg‐CRF is proposed by integrating DilaSeg with the RNN layers. Images containing three common types of sewer defects are collected from CCTV inspection videos and are annotated with ground truth labels, after which the proposed models are trained and evaluated. Experiments demonstrate that the end‐to‐end trainable DilaSeg‐CRF can improve the segmentation significantly, with an increase of 32% and 20% in mean intersection over union (mIoU) values compared with fully convolutional network (FCN‐8s) and DilaSeg, respectively. Our proposed DilaSeg‐CRF also achieves faster inference speed than FCN and eliminates the manual postprocessing for refining the segmentation results.     

Comparison of Structural Deterioration Models for Stormwater Drainage Pipes

Abstract:   Structural deterioration of pipes is the continuing reduction of load bearing capacity, which can be characterized through structural defects. Structural deterioration has been a major concern for asset managers in maintaining the required performance of stormwater drainage systems in Australia. Condition assessment using closed circuit television (CCTV) inspection is often carried out to assess the deteriorating condition of individual pipes. In this study, two models were developed using ordered probit and neural networks (NNs) techniques for predicting the structural condition of individual pipes. The predictive performances were compared using CCTV data collected for a local government authority in Melbourne, Australia. The significant input factors to the outputs of both models were also identified. The results showed that the NN model was more suitable for modeling structural deterioration than the ordered probit model. The hydraulic condition, pipe size, and pipe location were found to be significant factors for this case study.     

CCTV检测技术在排水管渠质量检测中的应用

环境保护问题越来越重要,由于排水管渠的破损、渗漏所造成的环境污染问题已为人们所重视,对于排水管渠的检测也成为关注的重点。CCTV检测是近年来引进的一种先进的管道内窥摄像检测技术。本文从技术介绍、检测实例、记录方法等方面对该检测技术进行了阐述,并提出了对其应用发展方面的建议。     

Inspection and maintenance planning of underground pipelines under the combined effect of active corrosion and residual stress

In this paper, a maintenance policy is proposed for pipelines subjected to active corrosion and residual stress, by taking into account imperfect inspection results. The degradation of the pipeline is induced by uniform corrosion, leading to losses of the pipe wall thickness. Localized corrosion is not considered herein, as neither pitting nor crevice corrosion are strongly influenced by external loading conditions and, hence, are not critical in structural strength considerations. When the corroded layers are removed, strain relaxation occurs, causing a redistribution of residual stresses. In parallel, the inspection is applied to detect the corrosion defects, namely the thickness of the corroded layer, and it has a detection threshold under which no corrosion rate can be measured. Due to uncertainties, each inspection is affected by the probability of detecting small defects and the probability of wrong assessment in terms of defect existence and size. The present work aims at integrating imperfect inspection results in the cost model for corroded pipelines, where the failure probabilities are computed by reliability methods. A numerical application on a gas pipe shows the influence of corrosion rates and residual stresses on the optimal maintenance planning.     

塑料排水管道破裂等级评价的分析研究

城镇排水管道在运行的过程中,常会由于各种原因造成管道破裂。这些破裂缺陷如何影响排水管道承载能力,是排水管道缺陷等级定义和评估要解决的问题。目前国内对排水管道破裂缺陷的研究比较有限,本文主要利用塑料管道作为模型试件,参照《城镇排水管道检测与评估技术规程》CJJ181-2012对破裂缺陷等级的划分,对试件模拟了叉形和圆形缺陷,并进行环刚度、抗压强度、外压破坏荷载试验研究,探讨管道破裂缺陷等级与缺陷数量的等效关系,以及缺陷位置和缺陷分布对管道破坏的影响。结果表明,1个2级叉形破裂缺陷与4个1级叉形破裂缺陷是等效的;1个4级圆形破裂缺陷与7个3级圆形破裂缺陷是等效的;相比排水管道顶部,管道侧壁是排水塑料管道的缺陷敏感区域;管道缺陷分布越均匀,对管道破坏程度越大。     

Automated defect detection for sewer pipeline inspection and condition assessment

To regularly and proactively assess conditions of sewer infrastructure systems to ensure their structural integrity and continuity of services, it is critical to advance the state of automated pipeline inspection and condition assessment. Currently, a critical issue is to address realistic defect detection that deals with real sewer inspection data. This paper presents the findings of a research project that seeks to enable automated detection of defects in sewer pipelines from inspection videos and images. The need for and the challenges of automated defect detection in sewer infrastructure condition monitoring are presented. Based on a general detection and recognition model established in this paper, a change detection based approach which is tailored to solve the challenges in this sewer pipeline domain is described and illustrated through case study.