Artificial Intelligence Assisted Infrastructure Assessment using Mixed Reality Systems
| Autor: | F. N. Catbas, Ulas Bagci, Enes Karaaslan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Subjectivity Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer science Computer Vision and Pattern Recognition (cs.CV) Mechanical Engineering Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction 0211 other engineering and technologies 020101 civil engineering 02 engineering and technology Mixed reality Human-Computer Interaction (cs.HC) Machine Learning (cs.LG) 0201 civil engineering Artificial Intelligence (cs.AI) Human–computer interaction 021105 building & construction Civil and Structural Engineering |
| Zdroj: | Transportation Research Record: Journal of the Transportation Research Board. 2673:413-424 |
| ISSN: | 2169-4052 0361-1981 |
| Popis: | Conventional methods for visual assessment of civil infrastructures have certain limitations, such as subjectivity of the collected data, long inspection time, and high cost of labor. Although some new technologies i.e. robotic techniques that are currently in practice can collect objective, quantified data, the inspectors own expertise is still critical in many instances since these technologies are not designed to work interactively with human inspector. This study aims to create a smart, human centered method that offers significant contributions to infrastructure inspection, maintenance, management practice, and safety for the bridge owners. By developing a smart Mixed Reality framework, which can be integrated into a wearable holographic headset device, a bridge inspector, for example, can automatically analyze a certain defect such as a crack that he or she sees on an element, display its dimension information in real-time along with the condition state. Such systems can potentially decrease the time and cost of infrastructure inspections by accelerating essential tasks of the inspector such as defect measurement, condition assessment and data processing to management systems. The human centered artificial intelligence will help the inspector collect more quantified and objective data while incorporating inspectors professional judgement. This study explains in detail the described system and related methodologies of implementing attention guided semi supervised deep learning into mixed reality technology, which interacts with the human inspector during assessment. Thereby, the inspector and the AI will collaborate or communicate for improved visual inspection. 5,240 word texts, 3 tables, 14 figures. Transportation Research Record: Journal of the Transportation Research Board, 2019 |
| Databáze: | OpenAIRE |
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