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Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed. [ABSTRACT FROM AUTHOR]

The underground sewer network is a vital public infrastructure in charge of large-scale wastewater collection and treatment. Complex defects can occur in sewer pipes due to various internal and external factors, which increase the demand for frequent inspection. Previous defect detection research mainly depended on manual inspection, which is tedious, costly, and error-prone. This study suggests an automatic pixel-level sewer defect segmentation framework based on DeepLabV3+, which can recognize the defect's type, location, geometric information and severity. The impacts of various backbones and pre-processing methods on the model's performance were carefully evaluated. In addition, four state-of-the-art segmentation models (U-Net, SegNet, PSPNet, and FCN) were compared with the presented model to demonstrate its superiority. The experimental results revealed that the DeepLabV3+ with the Resnet-152 backbone structure efficiently identified ten defect types under challenging conditions. The obtained mean pixel accuracy and mean intersection over union (IoU) were 0.97 and 0.68, respectively. In terms of severity analysis, it was revealed that the framework outputs were consistent with the NASSCO pipeline assessment certification program (PACP). In addition, during the testing process, the proposed frame reduction algorithm only required about 16% of the original time required to process an input video. Finally, with a generated detailed report for an inspection video, the suggested framework can offer a decision-making base for more precise and efficient defect inspection and maintenance. • A manually collected sewer defect segmentation dataset that contains over 11,124 images. • An efficient sewer defect segmentation framework based on DeepLabv3+. • A frame reduction is introduced to reduce the computational complexity. • Automatic report generation module to support real-life applications. • Defect severity analysis based on the NASSCO PACP program. [ABSTRACT FROM AUTHOR]

The sanitary sewer is a crucial underground infrastructure of any country that collects wastewater and carries it to the treatment plant. The damage triggered by various factors, such as external interference, long-term corrosion, and uneven distribution of pressure, could lead to various types of defects inside the sewer pipe. Previous studies primarily relied on human visual perception to evaluate the sewage system, which was tedious, time-consuming, and costly. As a result, an efficient and robust sewer defect localization framework was proposed in this manuscript. The main contributions include (1) a novel sewer defect detection system motivated by the state-of-the-art detection transformer (DETR) architecture, which views object localization as a set prediction topic; (2) a defect severity analysis approach based on the transformer's self-attention operation to analyze defect zone of influence and defect grade; and (3) a manually validated sewer defect localization dataset that contains 10 types of commonly appeared sewer defects. The experimental results suggested that the proposed system outperformed the previous standard object detection approaches with the highest mean Average Precision (mAP) of 60.2% on the collected dataset. • A manually collected sewer defect detection dataset that contains over 47,000 images. • An efficient transformer-based sewer defect detection framework. • Severity analysis of the localized defect using the transformer's self-attention weights. • The proposed model outperformed previous state-of-the-art object detection models. [ABSTRACT FROM AUTHOR]

• A dehazing algorithm is integrated into the proposed defect inspection framework in order to increase the detection rate. • An efficient backbone structure (Res2Net-Mish-BN-101) is proposed to improve the learning ability of SOLOv2. • An enhanced BiFPN (EBiFPN) is designed for better feature fusion. • A deep learning-based segmentation model (Pipe-SOLO) and a manually annotated dataset are firstly proposed to segment sewer defects at the instance level. The inspection of underground sewer defects plays a considerable role in estimating the structural integrity and avoiding various unforeseen functional failures. However, the conventional sewer defect inspection approaches suffer from the blurry and vaporous environment inside the sewer pipes, which significantly lowers the performance. Besides, it is challenging to achieve efficient and accurate condition assessment by the common manual inspection. Therefore, this manuscript introduces an automatic instance segmentation-based defect analysis framework. The main contributions include 1) a novel defect segmentation model called Pipe-SOLO is firstly presented to segment six common types of defects at the instance level by proposing an efficient backbone structure (Res2Net-Mish-BN-101) and designing an enhanced BiFPN (EBiFPN) , 2) a GAN-based dehazing model is applied to effectively solve the image blurring problem, and 3) a publicly available sewer defect segmentation dataset. The experimental results show the proposed Pipe-SOLO achieved an improvement of 7.3% compared with the state-of-the-art method in terms of the mean Average Precision (mAP). Therefore, the proposed defect segmentation method is promising to be integrated with real-life applications that require defect localization and estimation. [ABSTRACT FROM AUTHOR]