Automated sewer pipe defect tracking in CCTV videos based on defect detection and metric learning

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.

Section: Defect Inspectionmentioning

confidence: 99%

Section: Defect Inspectionmentioning

confidence: 99%

Section: Dataset and Evaluation Metricmentioning

confidence: 99%

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

Wang

Dang

et al. 2022

“…With the development of deep learning technology, it has been widely used in video processing [ 14 , 15 , 16 , 17 ], water level calculation [ 18 , 19 ], pipeline semantic segmentation [ 20 , 21 , 22 ], and pipeline defect classification [ 23 , 24 ] in the field of pipeline detection. Hassan et al [ 25 ] adopted AlexNet [ 26 ] into pipeline defect detection and used the images edited from pipeline CCTV videos to form the training set for the model.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Defect Detection System for Petrochemical Pipeline Based on Cycle-GAN and YOLO v5

Chen

Li³

et al. 2022

Defect detection of petrochemical pipelines is an important task for industrial production safety. At present, pipeline defect detection mainly relies on closed circuit television method (CCTV) to take video of the pipeline inner wall and then detect the defective area manually, so the detection is very time-consuming and has a high rate of false and missed detections. To solve the above issues, we proposed an automatic defect detection system for petrochemical pipeline based on Cycle-GAN and improved YOLO v5. Firstly, in order to create the pipeline defect dataset, the original pipeline videos need pre-processing, which includes frame extraction, unfolding, illumination balancing, and image stitching to create coherent and tiled pipeline inner wall images. Secondly, aiming at the problems of small amount of samples and the imbalance of defect and non-defect classes, a sample enhancement strategy based on Cycle-GAN is proposed to generate defect images and expand the data set. Finally, in order to detect defective areas on the pipeline and improve the detection accuracy, a robust defect detection model based on improved YOLO v5 and Transformer attention mechanism is proposed, with the average precision and recall as 93.10% and 90.96%, and the F1-score as 0.920 on the test set. The proposed system can provide reference for operators in pipeline health inspection, improving the efficiency and accuracy of detection.

“…In the literature, various algorithms and tools are reported to automate drain defect detection and mapping tasks. Closed-Circuit Television (CCTV) [3][4][5][6][7][8], sonar [9], laser scanner [10], infrared [11], and computer vision algorithms with robot-assisted remote inspection [12][13][14][15][16][17] are commonly used methods. Among them, computer vision-based robot-assisted remote inspection is a widely used method in the industry and has been classified into two categories: the traditional approach (non-learning) and the learningbased approach.…”

Section: Introductionmentioning

confidence: 99%

Drain Structural Defect Detection and Mapping Using AI-Enabled Reconfigurable Robot Raptor and IoRT Framework

Palanisamy

Elara

Semwal

et al. 2021

Human visual inspection of drains is laborious, time-consuming, and prone to accidents. This work presents an AI-enabled robot-assisted remote drain inspection and mapping framework using our in-house developed reconfigurable robot Raptor. The four-layer IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The Faster RCNN ResNet50, Faster RCNN ResNet101, and Faster RCNN Inception-ResNet-v2 deep learning frameworks were trained using a transfer learning scheme with six typical concrete defect classes and deployed in an IoRT framework remote defect detection task. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trials using the SLAM technique. The experimental results indicate that robot’s maneuverability was stable, and its mapping and localization were also accurate in different drain types. Finally, for effective drain maintenance, the SLAM-based defect map was generated by fusing defect detection results in the lidar-SLAM map.