Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey

Abstract

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.

A Sewer Pipeline Defect Detection Method Based on Improved YOLOv5

“…Compared to the YOLOv5s model, the proposed model exhibits a reduction of 30.1% in the number of parameters and a 29.4% decrease in computational complexity. (2) The model proposed in this study exhibits a high level of detection performance. The incorporation of the CBAM attention mechanism enhances the detection capability of the model, particularly in complex backgrounds.…”

“…Currently, closed-circuit television (CCTV) inspection is the most extensively employed method for pipeline inspection globally [2]. The process of CCTV inspection comprises two stages, namely, on-site video information collection and off-site evaluation.…”

System for 3D Acquisition and 3D Reconstruction using Structured Light for Sewer Line Inspection

“…There is an ample amount of literature, with most approaches relying directly on monocular images or video sequences as input (often with fisheye lenses), like (Xie et al, 2019;Künzel et al, 2018;Hansen et al, 2015;Zhang et al, 2011), with a recent survey regarding the detection and classification of defects in (Li et al, 2022). But as the literature shows, many previous approaches struggle with the detection of some spatial defects, like for instance misaligned pipe joints or bent pipes, as they are almost impossible to detect without depth information.…”

Quantitative Detection Technology for Geometric Deformation of Pipelines Based on LiDAR

“…This inefficiency leads to inadequate inspection and increased risk of human error. As a result, current research on CCTV technology focuses on transitioning from human-dependent pipeline condition assessment to automated machine recognition [ 8 , 9 ]. For instance, Dang et al [ 10 ] proposed a multi-framework ensemble detection model that extracts precise defect information from key frames of CCTV videos by leveraging different frameworks’ strengths.…”