Review of the State-of-the-art Sewer Monitoring and Maintenance Systems Pune Municipal Corporation - A Case Study

Patil, Ravindra R.; Ansari, Saniya M.; Calay, Rajnish Kaur; Mustafa, Mohamad Y.

doi:10.18421/tem104-02

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…In earlier work, a survey on sewer robotic systems and computer vision practices in sewer inspection works was carried out and that gave information about practical issues concerning sewerage systems under the Pune Municipal Corporation (PMC), India [ 11 ]. It was concluded that sewer blockage is the main issue of sewers in Pune and to date, there is no robust algorithm and robotic system available for both real-time detection and removal of sewer pipe blockages.…”

Section: Needs Of the S-bird Datasetmentioning

confidence: 99%

S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems

Patil

Mustafa

Calay

et al. 2023

Sensors

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Computer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages and damages. A large amount of appropriate, validated, and labeled imagery data is always a key requirement for learning AI-based detection models to generate the desired outcomes. In this paper, a new imagery dataset S-BIRD (Sewer-Blockages Imagery Recognition Dataset) is presented to draw attention to the predominant sewers’ blockages issue caused by grease, plastic and tree roots. The need for the S-BIRD dataset and various parameters such as its strength, performance, consistency and feasibility have been considered and analyzed for real-time detection tasks. The YOLOX object detection model has been trained to prove the consistency and viability of the S-BIRD dataset. It also specified how the presented dataset will be used in an embedded vision-based robotic system to detect and remove sewer blockages in real-time. The outcomes of an individual survey conducted at a typical mid-size city in a developing country, Pune, India, give ground for the necessity of the presented work.

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Section: Needs Of the S-bird Datasetmentioning

confidence: 99%

S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems

Patil

Mustafa

Calay

et al. 2023

Sensors

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“…This approach is intended for real-time implementation on mobile devices and other environments with limited resources, with the goal of effectively removing such blockages. Our primary emphasis is on the training of the single-stage YOLOv5 model using the S-BIRD dataset [20,21], which contains representative and critical multi-class images depicting prevalent sewer blockage scenarios.…”

Section: Introductionmentioning

confidence: 99%

AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems

Patil,

Calay,

Mustafa

et al. 2023

Electronics

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In artificial intelligence (AI), computer vision consists of intelligent models to interpret and recognize the visual world, similar to human vision. This technology relies on a synergy of extensive data and human expertise, meticulously structured to yield accurate results. Tackling the intricate task of locating and resolving blockages within sewer systems is a significant challenge due to their diverse nature and lack of robust technique. This research utilizes the previously introduced “S-BIRD” dataset, a collection of frames depicting sewer blockages, as the foundational training data for a deep neural network model. To enhance the model’s performance and attain optimal results, transfer learning and fine-tuning techniques are strategically implemented on the YOLOv5 architecture, using the corresponding dataset. The outcomes of the trained model exhibit a remarkable accuracy rate in sewer blockage detection, thereby boosting the reliability and efficacy of the associated robotic framework for proficient removal of various blockages. Particularly noteworthy is the achieved mean average precision (mAP) score of 96.30% at a confidence threshold of 0.5, maintaining a consistently high-performance level of 79.20% across Intersection over Union (IoU) thresholds ranging from 0.5 to 0.95. It is expected that this work contributes to advancing the applications of AI-driven solutions for modern urban sanitation systems.

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“…The reliability and durability of these networks are critical, given their interdependence with other systems. However, the retrofitting process of these networks can be intricate and multifaceted [2]. Over time, pipe networks experience deterioration, leading to various defects contingent upon factors such as the construction type of the pipes, environmental conditions, and the materials used.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Characterization of Cured-in-Place Pipe Defects

Dvořák,

Jakubka,

Topolář

et al. 2023

Materials

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Sewage and water networks are crucial infrastructures of modern urban society. The uninterrupted functionality of these networks is paramount, necessitating regular maintenance and rehabilitation. In densely populated urban areas, trenchless methods, particularly those employing cured-in-place pipe technology, have emerged as the most cost-efficient approach for network rehabilitation. Common diagnostic methods for assessing pipe conditions, whether original or retrofitted with-cured-in-place pipes, typically include camera examination or laser scans, and are limited in material characterization. This study introduces three innovative methods for characterizing critical aspects of pipe conditions. The impact-echo method, ground-penetrating radar, and impedance spectroscopy address the challenges posed by polymer liners and offer enhanced accuracy in defect detection. These methods enable the characterization of delamination, identification of caverns behind cured-in-place pipes, and evaluation of overall pipe health. A machine learning algorithm using deep learning on images acquired from impact-echo signals using continuous wavelet transformation is presented to characterize defects. The aim is to compare traditional machine learning and deep learning methods to characterize selected pipe defects. The measurement conducted with ground-penetrating radar is depicted, employing a heuristic algorithm to estimate caverns behind the tested polymer composites. This study also presents results obtained through impedance spectroscopy, employed to characterize the delamination of polymer liners caused by uneven curing. A comparative analysis of these methods is conducted, assessing the accuracy by comparing the known positions of defects with their predicted characteristics based on laboratory measurements.

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Review of the State-of-the-art Sewer Monitoring and Maintenance Systems Pune Municipal Corporation - A Case Study

Cited by 7 publications

References 20 publications

S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems

S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems

AI-Driven High-Precision Model for Blockage Detection in Urban Wastewater Systems

Non-Destructive Characterization of Cured-in-Place Pipe Defects

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