Developments in Computer Vision Based Monitoring and Inspection of Civil Infrastructure

Hassan Basri^*
*Correspondence: Hassan Basri, Department of Civil and Structural Engineering, University of Kebangsaan, Selangor, Malaysia, Email:

Description

Advances in computer vision-based civil infrastructure inspection and monitoring have heralded a transformative era in the management and maintenance of critical assets. The convergence of cutting-edge technologies has empowered engineers and inspectors with unprecedented capabilities to ensure the safety, efficiency, and resilience of infrastructure networks worldwide. One of the most striking advancements lies in the automation of inspection processes through sophisticated computer vision algorithms. By harnessing the power of artificial intelligence and image processing techniques, these algorithms can swiftly analyse vast quantities of visual data captured by cameras or drones. Gone are the days of laborious manual inspections; instead, inspectors now rely on automated systems to detect defects such as cracks, corrosion, or structural anomalies with unparalleled accuracy and efficiency. The proliferation of drone technology has been instrumental in revolutionizing civil infrastructure inspection. Equipped with high-resolution cameras and computer vision systems, drones enable inspectors to access inaccessible or hazardous areas without endangering themselves. From bridges spanning deep ravines to towering skyscrapers, drones provide a bird's-eye view, capturing detailed imagery that can be analyzed in real-time using computer vision algorithms. This aerial perspective not only enhances safety but also allows for comprehensive inspections that were previously impractical or impossible [1].

Machine learning, a subset of artificial intelligence, has propelled computer vision capabilities to new heights. These advanced algorithms can discern intricate patterns and anomalies in infrastructure imagery, continually refining their accuracy and reliability through iterative learning processes. By analyzing vast datasets, machine learning models can identify subtle indicators of deterioration or potential failures, empowering inspectors to preemptively address maintenance issues before they escalate. Three-dimensional reconstruction techniques have further augmented civil infrastructure inspection by enabling the creation of detailed digital models from captured imagery. Utilizing computer vision algorithms, these models accurately replicate the physical structure of bridges, buildings, and roads in threedimensional space. Inspectors can scrutinize these virtual replicas, pinpointing defects and assessing structural integrity with unparalleled precision. This immersive visualization not only facilitates more thorough inspections but also aids in the planning and execution of maintenance activities [2].

Augmented Reality (AR) has emerged as a powerful tool for enhancing the inspection process through real-time data overlay. By integrating computer vision with AR technology, inspectors can superimpose digital information onto live camera feeds, providing contextual insights directly within the inspection environment. From annotated annotations detailing structural components to historical maintenance records, AR empowers inspectors with comprehensive situational awareness, facilitating informed decision-making and expediting the inspection process. Remote monitoring has become increasingly prevalent in civil infrastructure management, thanks to advancements in computer vision technology. By deploying cameras and sensors equipped with computer vision capabilities, infrastructure operators can continuously monitor assets from afar, detecting anomalies or changes in real-time. This proactive approach to monitoring enables early intervention, preventing potential failures and minimizing downtime, ultimately ensuring the reliability and longevity of critical infrastructure networks.

The integration of computer vision with Internet of things sensors has further enriched infrastructure monitoring capabilities. By combining visual data with sensor-derived information such as temperature, vibration, or humidity, inspectors gain comprehensive insights into asset health and performance. This multi-sensor approach enables holistic monitoring, allowing inspectors to identify potential issues before they compromise structural integrity or operational efficiency. Data analytics and predictive maintenance have emerged as indispensable tools in the realm of civil infrastructure inspection and monitoring. By leveraging the vast amounts of visual data collected from inspections, computer vision systems can identify patterns and trends indicative of future maintenance needs. This predictive maintenance approach enables infrastructure operators to optimize maintenance schedules, allocate resources efficiently, and mitigate the risk of costly downtime. In conclusion, advances in computer vision-based civil infrastructure inspection and monitoring have ushered in a new era of safety, efficiency, and resilience. From automated defect detection to immersive 3D visualization, these technologies empower inspectors with unprecedented capabilities to safeguard critical assets and ensure the integrity of infrastructure networks. By embracing these innovations, infrastructure stakeholders can proactively address maintenance needs, enhance operational efficiency, and pave the way for a more sustainable future [3-5].

Acknowledgement

None.

Conflict of Interest

None.

References

1. Lawson, Anna, Ieva Eskytė, Maria Orchard and Dick Houtzager, et al. "Pedestrians with disabilities and town and city streets: From shared to inclusive space?." J Public Space 7 (2022): 41-62.

Google Scholar, Crossref, Indexed at

2. Bozovic, Tamara, E. Hinckson, T. Stewart and M. Smith. "How street quality influences the walking experience: An inquiry into the perceptions of adults with diverse ages and disabilities." J Urban Int Res Placemaking Urban Sustain 17 (2024): 111-136.

Google Scholar, Crossref, Indexed at

3. Hwang, Jinuk. "A factor analysis for identifying people with disabilities’ mobility issues in built environments." Transp Res Part F Traffic Psychol Behav 88 (2022): 122-131.

Google Scholar, Crossref, Indexed at

4. Levine, Kaylyn and Alex Karner. "Approaching accessibility: Four opportunities to address the needs of disabled people in transportation planning in the United States." Transp Policy 131 (2023): 66-74.

Google Scholar, Crossref, Indexed at

5. Maisel, Jordana L., Jimin Choi and Molly E. Ranahan. "Factors influencing fixed-route transit decision-making: Exploring differences by disability and community type." J Public Transp 23 (2021): 1-15.

Google Scholar, Crossref