Automated Vision Systems for Condition Assessment of Sewer and Water Pipelines
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Notice bibliographique
Résumé
Sewer networks and water distribution systems are among the most valuable and critical urban assets for a community. These systems have their respective time span to provide continuous service to the residents. Hence, it is paramount to assess the condition of sewer and water pipelines to ensure sustainable, reliable, and cost-effective transportation of sewerage and water supply. The assessment is usually done by the inspection robots, which are equipped with machine vision systems and/or sensors. The inspection robots acquire the inspection data, and the operators then conduct the survey of the captured video and/or sensory data to interpret the results. Nowadays, automated solutions are being adopted by industry to achieve an accurate and efficient assessment. This article surveys the state of the art of the automated vision systems, which are employed for condition assessment of sewer and water pipelines, and identifies the challenges for future research. The following areas are highlighted in this survey: 1) the typical types of the faults and failures, which include the concept and definition of the sewer and water pipelines; 2) the inspection systems, e.g., robotic platforms for sewer and water pipeline inspection; 3) the machine vision systems for fault detection; 4) the computational frameworks for condition assessment; and 5) the challenges and suggestions for future research. This article summarizes the current state of automation in the machine vision technology for condition assessment (both hardware and software perspectives) of sewer and water pipelines and also provides a reference for researchers to further advance the technology in this field. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation behind this article was the current challenges and the open issues of the vision system for the condition assessment of sewer and water pipelines. This article presents detailed reviews of the state-of-the-art of the vision system (both hardware and software) and discusses the existing problems. This survey aims to help engineers and researchers to resolve and improve the problems and extend the field of the existing automated frameworks. This article is organized in the form of a survey so that researchers can benefit and get all the useful information at a glance. This article provides a rigorous overview of typical faults, inspection robots, visual techniques, and automated frameworks for the condition assessment and discusses the challenges and future research directions. The objective of this article is to create a baseline for the readers to acquaint themselves with the state of the art and advance the research in this field.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle