By Anoop G Prabhu, Co-Founder & CTO, Vehant Technologies
Deep tech innovation now serves as the primary technological foundation that supports modern infrastructure development, with multiple advanced technologies, such as artificial intelligence (AI), intelligent surveillance systems, automation technologies, and real-time analytics and sensor-based systems, that have already changed how global industries and essential infrastructure systems function. The development of indigenous innovation systems now holds equal value as countries work to create their own deep tech solutions, which address their unique domestic problems and structural needs throughout their public infrastructure systems. The Indian mobility system now demonstrates a seamless transition into intelligent transportation systems, which build advanced safety features, efficient methods of handling traffic, and protective measures for their growing transit networks, including multiple modes of travel across various routes.
Deep tech innovations are now increasingly being driven by strong industry-academia collaboration, where big tech industries bring technological expertise, real-world deployment experience, and industry-led challenges, whereas academic institutions contribute with research-driven innovation, engineering talent, and advanced ideas. This collaborative ecosystem is accelerating the development of scalable and indigenous deep tech innovations across the sectors, enabling solutions that are both innovation-led and practically deployable at scale.
This transformation is now becoming highly visible in India’s infrastructure network, where growth is no longer defined only by the number of highways being built or the scale of new airports and smart cities taking shape. With the world’s second-largest road network, India is witnessing a major shift in intelligent transportation and security ecosystems. But the real shift lies in how technology is becoming deeply embedded in these expanding networks. If we closely look at India’s transport infrastructure expansion over the last few years, roads and highways stand out as one of the biggest examples of this shift. Along with the increase in kilometres, there has also been a significant rise in the adoption of intelligent technologies, helping authorities manage safety, security and operations more efficiently.
Earlier, several accident-prone stretches on highways, crime-prone zones in cities and traffic monitoring areas were difficult to manage and continuously monitor. The presence of sharp turns, blind spots, high-speed corridors, and isolated stretches created significant obstacles to conducting real-time surveillance and responding to incidents. The resolution of these difficulties needs governmental and infrastructural organisations to establish AI-based surveillance systems that deliver continuous 24X7 monitoring throughout all roadways and highways. AI accelerators represent one such layer of deep-tech innovation that further augments AI system capabilities, with on-board NPUs now being integrated directly on-silicon within edge devices, pushing real-time inference closer to the source and enhancing the overall responsiveness of surveillance and monitoring systems. The intelligent systems possess the ability to detect real-time anomalies, which encompass the identification of stationary vehicles, the observation of unusual traffic patterns, and the measurement of road congestion and blockage situations.
The deployment of intelligent systems extends beyond highways and smart cities to large public events where AI-driven video analytics assist authorities in monitoring crowd movements, identifying suspicious behaviours, and handling security emergencies. The implementation of such technologies results in improved public safety, together with enhanced operational efficiency and expedited decision-making processes for authorities.
Another significant application of AI-powered surveillance technology is in crime mapping. Today’s infrastructure networks are not limited to being transport corridors but have also become critical security zones where incidents such as vehicle theft, hit-and-run cases, and suspicious movements are frequently reported. The traditional surveillance systems required manual observation to record their observations, while organisations used their footage to investigate crimes after they occurred, which created challenges for law enforcement to identify vehicles in criminal activities. The integration of AI-based video analytics with Automatic Number Plate Recognition (ANPR), real-time monitoring systems, and intelligent data processing enables authorities to increase their detection capabilities, which allows them to track and respond to suspicious activities with improved speed and accuracy.
Indigenous deep tech beyond digital infrastructure
The use of indigenous deep tech innovations now extends beyond their initial application for road and highway infrastructure to critical systems that protect airports, railway stations, metros, judicial courts, hotels, and shopping mall security points. Security authorities begin to use intelligent systems that provide them with real-time threat assessment capabilities, because security threats have reached more sophisticated levels.
The first instance of deep tech implementation in contemporary security systems occurred when authorities introduced X-ray baggage screening technology, which allowed them to examine bags, parcels, and cargo without doing manual inspections. The systems have achieved widespread deployment in airports and metro stations; government buildings and border checkpoints; and other public areas that require high-security measures because they now use multi-energy imaging, AI threat detection, real-time image processing, and automated object identification. The deep tech-driven evolution of this system has achieved better screening accuracy and lower human error rates; improved operational efficiency; and faster detection of explosives, weapons, and hidden threats.
As security threats evolved further, the challenge became more serious with the emergence of non-metallic weapons, concealed explosives, narcotics, and plastic-based materials that are difficult to identify through traditional screening methods. And that, in turn, has pushed the adoption of deep tech security solutions, where AI-enabled threat detection, advanced sensors, real-time analytics, and intelligent screening tools are helping authorities spot suspicious materials and concealed threats with much greater speed and precision.
India today also has its own patented, world’s first microsensor-based explosive trace detector (ETD), capable of detecting nanogram quantities of explosives and delivering results within seconds. Such indigenous deep tech innovations are strengthening the country’s security infrastructure while reducing dependence on imported high-security technologies.
Examples from physical infrastructure to intelligent infrastructure networks
The implementation of advanced AI surveillance systems together with intelligent analytic systems, automation tools, and real-time monitoring systems now operates throughout major motorway corridors, which include the Bengaluru-Mysuru Motorway, Dwarka Motorway, Ganga Motorway, and Sikar-Bikaner National Highway, to improve traffic control systems, road safety measures, and operational performance.
These deep tech-driven systems help authorities move from their outdated manual monitoring system to complete infrastructure control through their ability to detect incidents faster, enforce rules automatically, and create real-time responses and emergency management systems that use improved coordination. The intelligent infrastructure systems in Gurugram, Raipur, Ranchi, Nagpur, and Visakhapatnam smart city projects enable city officials to gather data, which helps them manage traffic flow better, improve urban transportation systems, enhance public security, and streamline citywide operations through real-time situation monitoring.
At the same time, indigenous deep tech innovations in security screening systems, under-vehicle scanning technologies, explosive detection systems, and AI-driven command-and-control platforms are helping authorities transition from traditional reactive monitoring to intelligent real-time security ecosystems. With the integration of AI/ML-powered analytics and connected surveillance infrastructure, edge computing for AI plays a critical enabling role by processing data directly at the source. Whether at smart city junctions, highway corridors, or airport terminals, it drastically reduces latency and enables faster autonomous decision-making at the edge. Today, authorities can monitor large-scale public events more efficiently, reducing emergency response time and strengthening overall public safety and operational efficiency.
The deep tech transformation process is becoming increasingly visible across India’s aviation system. Recently, the country has implemented explosive trace detection systems at more than 60 airports, which now enhance both aviation security and passenger screening operations. Today, cities such as Mumbai, Bengaluru, Hyderabad, Chennai, Kolkata and so on are equipped with advanced baggage screening systems and intelligent security technologies, which enable authorities to improve operational efficiency and enhance threat detection capabilities across critical public infrastructure.
Indigenous deep tech innovation has become essential for developing India’s future network of intelligent, connected, and secure infrastructure systems as the country expands its highways and airports, metro systems, railway networks, and smart cities.