Building safer & smart roads with computer vision for highway asset monitoring

In modern society, transportation is considered key to successful road connectivity. The safety improvement is paramount to modern urbanization as it keeps a firm check on the traffic and ensures a smart city infrastructure. Nowadays, computer vision is successfully dominating diverse global industries ranging from finance and healthcare to automotive and emergency industries.

Keeping this in mind, city planners are optimizing the functionality of computer vision to benefit both drivers and road safety projects in the long term. When it comes to any urban infrastructure, transportation is considered primordial to optimizing highway and road safety assets by systematically harnessing computer efficiency. Age-old infrastructure, limited inspection resources, and increasing traffic volumes make traditional highway monitoring reactive and expensive. Herein lies the importance of computer vision systems in revolutionizing roads into smart urban infrastructure.

In this blogpost, we are going to discuss the transportation of road safety as a result of computer vision models. This blog discusses the benefits and challenges of using this technology and assesses its future aspects.

What are the applications of computer vision technology in transportation?

Computer vision is rising as the most useful technology in preventing serious traffic issues and road congestion. As a powerful technology, not only does it introduce cost-efficient ways of administering incident detection, driver monitoring, and highway management solutions, but also create a secure urban city infrastructure with real-time surveillance.

Maintaining road & highway safety

The major aspects of highway asset management are road safety and effectiveness. Leveraging the traditional ADAS and intelligent transportation systems can rely on sensing the areas around the vehicle. On the contrary, computer vision elevates the process with advanced analytics and an increased capability on the gathered information. The integration of computer vision for highway safety has undoubtedly boosted roadside safety besides facilitating law enforcement. Vision AI uses AI-powered data (video/image) analysis to automatically scan, classify, and evaluate highway assets in real time. It ensures greater road safety and reduces potential vehicle collision.

Analytics supported by video sensing.

Today, with a greater number of businesses engaging in AI-enabled technologies, why should highway management authorities stay away? Video cameras integrated with AI Video Analytics for Highway Asset Monitoring bring the benefit of reduced costs and hassle-free installations with Vision AI’s sensing and recording potentials. Without the need of any manual intervention, vision AI systems can perform the dual trick of analytic output.

Guardrail & barrier monitoring

Specially designed to lower the severity of road accidents, crash barriers and guardrails are vital to road safety. But their constant exposure to vehicle impacts, corrosion, weather conditions, and structural fatigue is weakening the very concept of guardrails. Here comes the functionality of computer vision-based monitoring in constantly keeping a check on the condition of assets with higher speed, accuracy, and safety.

Real-time alerts & warnings

Integrating real-time alert systems are highly beneficial in situations of emergency. For instance, of child abduction or other unfortunate incidents, Vision AI is empowered to detect them in real-time, trigger alerts, and notify security personnel for prompt action. The traditional modes of vigilance and preventing incidents often require time to react in a situation. However, this isn’t the scene with Computer vision, as it enables time-effective scrutiny of large databases and takes action in real time. With the ever evolving video analytic algorithms combined with advanced machine learning curates a positive venture for robust incident detection. It contributes to shorter response times and improved efficiency of first responders.

Pedestrian safety

One of the significant areas of concern in urban road infrastructure is pedestrian safety, where computer vision technology can contribute effectively. Cameras mounted at crosswalks can be integrated with AI Video Analytics software to detect pedestrian movement and alert drivers. Vision AI models can enable smart traffic lights to automatically adjust their cycle timing in coherence with real-time pedestrian movement. During high-volume traffic, it can analyze foot traffic and extend footpath crossing time. With real-time monitoring, it ensures pedestrian safety and adds to the comprehensive well-being of the nation.

Drainage & waterlogging detection

For maintaining highway safety, an effective drainage system is influential. Warelogging, blocked drains, and poor water conditions can add to the degradation of pavements. Besides increasing road accident risks, it also lowers visibility. Computer vision AI models can detect waterlogs and allow highway authorities to monitor, scan, and address these items proactively. The system continuously ensures real-time and historical visibility into drainage health.

Highway infrastructure efficiency​

The computer vision system has slowly infiltrated this domain to upgrade incident detection and response periods. Having traffic-related data acquired in real time, local government authorities can boost their incident management abilities. The modern technology improves highway infrastructure efficiency by automated asset monitoring, early defect detection, predictive maintenance planning, advanced road safety, and reduced traffic disruptions along with data-driven decision making.

Predictive maintenance & planning

By analyzing historical and real-time visual data, computer vision is capable of predicting asset low-maintenance patterns, enabling maintenance teams to plan repairs. It eliminates costly affairs, replaces reactive repairs with risk-based decisions advancing safety, lowering costs, and extending the overall asset life. Predictive maintenance powered by computer vision combines real-time monitoring, historical data, and AI models to identify patterns of wear and deterioration.

Flow of traffic

Several highways have already begun to witness the installation of roadway sensors and induction coils. However, they lack accuracy and require costly maintenance. Computer vision just cropped up at the right time with automated video analytics and real-time video processing. Vision AI systems can successfully implement video sequences for evaluating traffic conditions and critical hours.

Why choose Nextbrain’s highway asset monitoring systems powered by Computer vision?

The highway asset monitoring system by Nextbrain leverages cutting-edge computer vision and deep learning algorithms to analyze video feeds in real time. It allows automated detection of defects, anomalies, and hazards without manual intervention, making monitoring seamless. Contrary to manual inspections, solutions delivered by Nextbrain can continuously process visual data from AI-integrated cameras  and analyze them to identify wear, damage, and missing assets at an early stage, allowing for predictive and preventive maintenance instead of major expenses.

Properly designed to scale across large highway networks, the solution can integrate seamlessly with existing cameras, asset management systems, and drones for delivering operational efficiency and safer highways.

Final thoughts

The combination of real-time data on traffic conditions with an AI video analytics system can not only derive utmost benefit but also create a roadway infrastructure. Computer vision is redefining highway asset management by means of transforming the ways road infrastructure is monitored, maintained as well and optimized. By allowing continuous, accurate, and automated inspection of highway assets, computer vision eradicates the restrictions of manual surveys and introduces a proactive maintenance approach.

Ready to take the urban road infrastructure to the next level? Get in touch with our professionals to know more about computer vision for smart highway road management.  

Frequently asked questions

What is computer vision in highway asset management?      

In highway asset management, AI utilizes AI algorithms to analyze image and video from cameras and vehicles. It can automatically detect, monitor, and evaluate the condition of road assets.

What types of highway assets can be monitored using computer vision?

Computer vision is capable of monitoring a wide range of objects comprising road signs, crash barriers, guardrails, traffic lights, lighting poles, pavements, and roadsign.

How does computer vision improve road safety?

Computer vision identifies missing, damaged, or poorly visible highway assets in real time. Therefore, after scanning, it enables authorities to fix safety risks prior to them causing accidents. It promotes early detection of glitches such as faded lane markings, broken barriers, directly improving driver safety.

Do you ensure computer vision supporting predictive maintenance for highways?

Yes, AI-powered computer vision allows predictive maintenance by identifying early signs of damage or degradation. It enables traffic organizations and teams to act before situations escalate to major events. From reducing repair costs to preventing any kind of unexpected road accidents and failures, computer vision traffic monitoring and incident management systems help in lowering highway failures.

How do you measure computer vision’s scalability for highway asset management?

Scalability of computer vision for highway asset management can be measured by its capability to process large volumes of image and video data across different road networks. Amidst maintaining real-time performance and low operational costs, the computer vision system curates accurate results. Vision AI systems are powerful and scalable, and can be deployed across city roads and national road networks. It is highly ideal for large-scale infrastructure management.

What is the future of highway asset management?

AI-empowered infrastructure management lays forth a complete autonomous urban road structure. It focuses on real-time insights, smart automation, and predictive analytics working together to deliver more resilient and safer road networks.