Why 'Vision AI' is Essential for Underground Highways
- Detecting Even 'Invisible' Risks Through Smart Safety Technology
- Synthetic Data: Extracting High-Quality Data with Less Time and Cost
Hyun-joo Kim, CEO of Another Real
While underground highways are gaining attention as key infrastructure to resolve urban traffic congestion, a fatal risk lies behind them: their closed structure. In the event of disasters such as fires, flooding, or structural collapses, evacuation and rescue are highly challenging, and minor incidents carry a high probability of escalating into mass casualties.
In this environment, safety technology must evolve beyond merely detecting accidents into an intelligent system that captures early warning signs and triggers immediate responses. At the center of this transformation lies Vision AI.
■ "Situational Awareness" Capabilities Formed by Moving Beyond the Limits of Single Sensors
Currently, various physical sensors—such as water level sensors, temperature sensors, and smoke detectors—are utilized in underground highway safety management. However, they possess distinct limitations. Sensors only detect a single physical quantity (water, heat, or smoke) and fail to understand the overall context of the site.
Actual accident scenes are complex. Following a vehicle collision, a fire breaks out, and the resulting smoke obscures visibility while evacuating crowds become entangled. Such complex situations cannot be interpreted solely through individual sensor signals.
This is where the necessity of Vision AI emerges. Vision AI is a technology that comprehensively understands an entire scene within a frame rather than relying on a single numerical value. Moving beyond simple object detection, it identifies risk factors through behavior recognition and hazard classification. This provides genuine "situational awareness" capabilities that conventional sensor technologies could not deliver.
■ Technology that Pierces Through Darkness and Smoke to Confront Risks Directly
Vision AI is particularly indispensable for overcoming low-light conditions and poor visibility, which are chronic issues for underground highways. General monitoring systems easily become useless when lights are turned off or dimmed. However, advanced Vision AI technology classifies objects by analyzing silhouettes in black-and-white inverted video even in low-light environments, and treats the drop in illumination itself as a danger signal to issue warnings. This goes beyond simple video surveillance, technologically compensating for blind spots that the human eye might miss.
The integration of Vision AI and thermal imaging technology also plays a decisive role in securing the golden time for disaster response. Conventional smoke detectors only operate after smoke has spread beyond a certain concentration. In contrast, thermal imaging-based Vision AI captures "precursors" before a fire even breaks out by detecting overheating in a vehicle's engine bay or abnormal temperature rises in tunnel structures. Furthermore, even in zero-visibility situations filled with smoke, it can pinpoint locations by sensing human body heat. Based on this AI-analyzed data, the system can dispatch evacuation guidance signals in the opposite direction, thereby increasing survival rates.
Flooding responses are also drastically improved. Unlike physical water level sensors, which carry the risk of malfunction, Vision AI directly recognizes the water line within the camera footage and cross-validates it with water level sensor data to increase the accuracy of flooding assessments. Furthermore, it analyzes the rate of flooding to activate vehicle entry barrier systems before reaching critical water levels, allowing detection, judgment, and control to function as a single, organic workflow.
■ "Synthetic Data": The Core of Efficiency and the Solution to Data Scarcity
However, no matter how outstanding an AI is, it is useless without data to learn from. The conventional method of collecting and labeling data from actual sites consumes vast amounts of time and capital, and securing data for rare disaster scenarios is virtually impossible.
This is precisely where synthetic data technology becomes an essential solution. Through virtual simulations, it can generate an infinite pool of high-quality data in a fraction of the time and cost required conventionally. Another Real utilizes this technology to proactively train its systems on all plausible scenarios, including fires and flooding. Moving beyond a simple technological supplement, this will establish itself as a new technical standard for building safety systems in underground spaces in the most efficient and economical manner.
Safety on underground highways can no longer remain a matter of reactive, after-the-fact responses. The system must "see" the risk and "understand" it before an accident occurs. Equipped with scalability that transcends physical limitations through synthetic data, Vision AI technology integrated with various sensors is the most definitive solution to transform the uncharted space of the underground into a safe and smart infrastructure.
Source: Korea Construction News
Why 'Vision AI' is Essential for Underground Highways
- Detecting Even 'Invisible' Risks Through Smart Safety Technology
- Synthetic Data: Extracting High-Quality Data with Less Time and Cost
Hyun-joo Kim, CEO of Another Real
While underground highways are gaining attention as key infrastructure to resolve urban traffic congestion, a fatal risk lies behind them: their closed structure. In the event of disasters such as fires, flooding, or structural collapses, evacuation and rescue are highly challenging, and minor incidents carry a high probability of escalating into mass casualties.
In this environment, safety technology must evolve beyond merely detecting accidents into an intelligent system that captures early warning signs and triggers immediate responses. At the center of this transformation lies Vision AI.
■ "Situational Awareness" Capabilities Formed by Moving Beyond the Limits of Single Sensors
Currently, various physical sensors—such as water level sensors, temperature sensors, and smoke detectors—are utilized in underground highway safety management. However, they possess distinct limitations. Sensors only detect a single physical quantity (water, heat, or smoke) and fail to understand the overall context of the site.
Actual accident scenes are complex. Following a vehicle collision, a fire breaks out, and the resulting smoke obscures visibility while evacuating crowds become entangled. Such complex situations cannot be interpreted solely through individual sensor signals.
This is where the necessity of Vision AI emerges. Vision AI is a technology that comprehensively understands an entire scene within a frame rather than relying on a single numerical value. Moving beyond simple object detection, it identifies risk factors through behavior recognition and hazard classification. This provides genuine "situational awareness" capabilities that conventional sensor technologies could not deliver.
■ Technology that Pierces Through Darkness and Smoke to Confront Risks Directly
Vision AI is particularly indispensable for overcoming low-light conditions and poor visibility, which are chronic issues for underground highways. General monitoring systems easily become useless when lights are turned off or dimmed. However, advanced Vision AI technology classifies objects by analyzing silhouettes in black-and-white inverted video even in low-light environments, and treats the drop in illumination itself as a danger signal to issue warnings. This goes beyond simple video surveillance, technologically compensating for blind spots that the human eye might miss.
The integration of Vision AI and thermal imaging technology also plays a decisive role in securing the golden time for disaster response. Conventional smoke detectors only operate after smoke has spread beyond a certain concentration. In contrast, thermal imaging-based Vision AI captures "precursors" before a fire even breaks out by detecting overheating in a vehicle's engine bay or abnormal temperature rises in tunnel structures. Furthermore, even in zero-visibility situations filled with smoke, it can pinpoint locations by sensing human body heat. Based on this AI-analyzed data, the system can dispatch evacuation guidance signals in the opposite direction, thereby increasing survival rates.
Flooding responses are also drastically improved. Unlike physical water level sensors, which carry the risk of malfunction, Vision AI directly recognizes the water line within the camera footage and cross-validates it with water level sensor data to increase the accuracy of flooding assessments. Furthermore, it analyzes the rate of flooding to activate vehicle entry barrier systems before reaching critical water levels, allowing detection, judgment, and control to function as a single, organic workflow.
■ "Synthetic Data": The Core of Efficiency and the Solution to Data Scarcity
However, no matter how outstanding an AI is, it is useless without data to learn from. The conventional method of collecting and labeling data from actual sites consumes vast amounts of time and capital, and securing data for rare disaster scenarios is virtually impossible.
This is precisely where synthetic data technology becomes an essential solution. Through virtual simulations, it can generate an infinite pool of high-quality data in a fraction of the time and cost required conventionally. Another Real utilizes this technology to proactively train its systems on all plausible scenarios, including fires and flooding. Moving beyond a simple technological supplement, this will establish itself as a new technical standard for building safety systems in underground spaces in the most efficient and economical manner.
Safety on underground highways can no longer remain a matter of reactive, after-the-fact responses. The system must "see" the risk and "understand" it before an accident occurs. Equipped with scalability that transcends physical limitations through synthetic data, Vision AI technology integrated with various sensors is the most definitive solution to transform the uncharted space of the underground into a safe and smart infrastructure.
Source: Korea Construction News