Harnessing Sight: Understanding Computer Vision in CNI Industries

Many of you might have used DigiYatra at Indian airports or SmartGates at Dubai Airport, or similar systems elsewhere in the world and wowed at the experience. How about we explore the technology behind these amazing applications? Welcome to the fifth installment of our ongoing series on artificial intelligence (AI). This article focuses on a significant area of AI: Computer Vision (CV).

Computer vision is a critical subfield of AI that imparts human-like sight to machines. Through digital image processing and complex algorithms, it enables machines to comprehend and make decisions based on visual data.

In this article, we will traverse the origins of computer vision, delineate the technological components powering it, and explore its functionality. We will discuss its widespread applications across diverse sectors, with a specific emphasis on its roles in telecommunications and its utility for safety and security within Critical National Infrastructure (CNI) industries.

The Genesis of Computer Vision

The birth of computer vision can be traced back to the 1960s when Larry Roberts at MIT published his doctoral thesis on the possibility of extracting 3D geometric information from 2D views. The field of computer vision was born with the objective to allow computers to gain high-level comprehension from images or videos. From object recognition and event detection to even learning, computer vision strives to replicate the capabilities of human vision.

The growth of computer vision has been exponential. The advancements in technology, the surge in computational power, and the availability of large-scale image databases have powered this rapid evolution. Today, we see applications of computer vision in various domains, from healthcare to autonomous vehicles, and importantly, in Critical National Infrastructure (CNI) Industries.

The journey of computer vision has been quite remarkable. From being a concept in the 1960s to now being a cornerstone of many industry operations, the development of computer vision has been truly outstanding. And it is interesting to note that this is just the beginning.

Technologies Powering Computer Vision

Computer vision is not a single technology but a synergy of several. Image recognition, machine learning algorithms, neural networks, and deep learning are all instrumental technologies that have come together to make computer vision possible.

  1. Image Recognition: It involves identifying and detecting an object or feature in a digital image or video. It forms the base for computer vision applications.
  2. Machine Learning Algorithms: These are the backbone of computer vision systems. They provide the system the ability to learn from and make decisions based on data, improving its performance over time.
  3. Neural Networks: Inspired by the human brain, neural networks help systems learn from vast amounts of data, improving accuracy in recognizing and interpreting complex patterns. Convolutional Neural Networks (CNNs) are a special type of neural network architecture designed specifically for processing grid-like data such as images. CNNs have proven to be exceptionally successful in tasks like image classification, object detection, and face recognition due to their ability to automatically learn and extract features from images, unlike traditional machine learning algorithms.
  4. Deep Learning: A subfield of machine learning, deep learning uses complex neural networks to process layers of data, increasing the precision and sophistication of image recognition and interpretation.

Each of these technologies works in tandem to create a computer vision system. For example, a deep learning algorithm, powered by a neural network, might be used to improve the accuracy of image recognition in a computer vision system.

Workings of Computer Vision

Understanding the workings of computer vision requires understanding the stages of image processing and interpretation.

Understanding Computer Vision-Workings of Computer Vision
  1. Image Acquisition: This is the first stage where an image or video is captured by a sensor (like a camera).
  2. Preprocessing: The captured images are then prepared for analysis. This might involve reducing noise, adjusting the size or color, or other transformations that make the images easier for the system to analyze.
  3. Feature Extraction: The system then identifies and isolates distinct structures or elements within the images. This can include lines, edges, and points of interest.
  4. Detection/Recognition: The preprocessed data and extracted features are used to recognize patterns, objects, or details. Here, Convolutional Neural Networks (CNNs) play a significant role. They are designed to automatically and adaptively learn spatial hierarchies of features from the provided data. For instance, in an image, CNNs can identify shapes, faces, or specific objects, depending on the application.
  5. Interpretation: After detection, the system interprets what it “sees.” For example, a system could identify an object, recognize it as a pedestrian, and predict their intended movement direction.

The goal of each of these stages is to transform visual data into a form that a computer can understand and use to make decisions or predictions. The effectiveness of computer vision depends largely on the efficiency and accuracy of each of these steps.

The core of Computer Vision is the application of machine learning. The system is exposed to a large number of images, allowing it to learn and identify patterns. This training process makes Computer Vision systems highly efficient and precise.

Applications of Computer Vision in CNI Industries and Beyond

Computer Vision has found its place in numerous applications. In healthcare, it is being used for early detection of diseases like cancer. In the retail industry, it’s being used for automated checkout systems. In automobiles, it’s being used for autonomous driving. However, in the context of CNI industries, two use cases stand out – AI for Preventive Maintenance and Computer vision for Anomaly Detection.

In Preventive Maintenance, Computer Vision (in conjunction with drones) is used to automate visual inspections, reducing human exposure to hazardous conditions and saving time and money. For Anomaly Detection, Computer Vision algorithms scan through video footage to identify anomalies or irregularities that might indicate potential threats.

Computer Vision is not only a potent standalone solution, but its influence on the emergence of autonomous vehicles, especially drones, is visible all around us. From agriculture to disaster management, from wildlife conservation to construction and real estate, and from visual inspections to delivery services, tight integration between computer vision and drones has benefitted a host of industries. Similarly, the autonomous vehicles sector, with far more stringent safety and regulatory requirements, is dependent on advances in computer vision to achieve its goals.

Computer Vision in the Telecommunication

In the Telecommunication sector, Computer Vision has become instrumental in optimizing operations. The most prominent adoption of Computer Vision has already happened in the form of drone-based tower (or other remote infrastructure) inspections. Telecommunication leaders all around the world have deployed CV-enabled drones to automate the inspections of their vast remote tower infrastructure.

Apart from tower inspection (and other visual inspection applications), Computer Vision has numerous other use cases in telecommunications, like efficient cable layout planning by analyzing images and videos of dense urban areas, automated remote support (say to install a home router or providing support to an enterprise client at a remote location) by utilizing feed from a user’s cellphone camera. Utilization of Computer Vision to provide real-time onsite support have huge potential in CNI industries as well, where the locations are generally remote but have good network connectivity.

Computer Vision for Security and Safety Applications in CNI Industries

You can find numerous examples of Computer Vision in Security and Safety domains across industries. For CNIs where security and safety are default critical parameters, Computer Vision has already started to gain prominence. For security applications, Computer Vision can be used for face recognition, intrusion detection, and surveillance. Its ability to analyze vast amounts of visual data in real-time makes it ideal for these tasks.

All CNIs, by default, have varying levels of CCTV coverage, thus already satisfying the first requirement (i.e., image acquisition) to implement computer vision. On the edge, we are seeing the emergence of intelligent cameras, which provides numerous onboard analytics, and similarly, on the server side, NVMS vendors have already incorporated CV-based analytics in their platform and continue to improve and expand their offerings. Although traditional CCTV systems have had video analytics components for decades, they were affordable only to large installations due to the requirements of specialized hardware and licensing costs. Now with the emergence of edge analytics and cloud-based NVMS offerings, intelligent CCTV systems are becoming cost-effective and hence now being increasingly adopted by CNI industries. Advancements in drone technology are also facilitating the adoption of CV in areas like pipeline monitoring.

CNIs, and specifically the energy sector, must deal with hazardous materials and hence hazardous environments. CNIs have a robust safety eco-system from established standards (like ATEX and NEC) to specify hazardous areas and govern the equipment/ components used in hazardous areas to industry-specific detailed safety procedures. CNIs in energy (and specifically in Oil and Gas sector) often have dedicated Process CCTV systems to monitor process areas. For safety applications, Computer Vision can boost the effectiveness of traditional CCTV systems to the next level. Today, Computer Vision can detect hazards such as smoke and fire, and gas leakage. Computer vision can also be employed to check HSE compliance (PPE detection, adherence to safety procedures, etc.) and potential safety hazards (unstable structures, missing safety structures like scaffolding, etc.). Traditionally, during construction, the site had minimal CCTV coverage, but now tethered or flying drones equipped with CV provide fast and reusable CCTV infrastructure during construction phases. Further, CV-equipped land-based autonomous vehicles can go to areas where humans cannot operate safely. Today, we are used to bulky cameras with explosion-proof housings for our process monitoring requirements, but imagine the landscape when lightweight, intrinsically safe cameras equipped with edge intelligence are available for deployment.

While CCTV systems were always a norm in CNI industries, the monitoring was a manual process. If we visualize a plant with thousands of cameras, the inefficiencies of manual monitoring for both security and safety become evident, there is always a chance that an important event missing due to operator fatigue or technical glitch in control room monitors, etc. What computer vision brings to the table is the ability to identify potential threats and anomalies and present them in real-time to the concerned parties (operators, security, HSE, etc.), thus freeing the operators from watching video walls 24×7 so that they can focus on effective incident response. It won’t be far-fetched to say that in a few years, computer vision will become synonymous with CCTV monitoring across CNI sectors.

The use of computer vision in security and safety applications in CNI Industries has and will continue to significantly enhance overall safety standards, creating safer and more secure environments for CNI industries.

Challenges in Computer Vision Adoption in CNI Industries

While the potential of computer vision for CNI industries is significant, the path to its widespread adoption is not without challenges. Understanding these can help decision-makers plan more effectively and manage expectations as they move forward with implementing computer vision solutions. Here are some of the key challenges:

Understanding Computer Vision-Challenges in Computer Vision Adoption in CNI Industries
  1. Data Privacy and Security: CNI sectors handle sensitive data, including personally identifiable information, which makes data security paramount. It’s essential that computer vision systems are designed with robust security measures to prevent data breaches.
  2. Regulatory Compliance: In many CNI indu stries, regulatory considerations can complicate the deployment of computer vision. For instance, facial recognition technologies may run into privacy regulations, and drone usage may be limited by aviation authorities.
  3. Data Quality and Volume: The performance of computer vision systems often depends on the quality and quantity of the data they are trained on. Gathering high-quality, diverse, and representative data in sufficient quantities can be a challenging task.
  4. Technical Expertise: Implementing and maintaining computer vision systems requires a level of expertise that may not be readily available in many organizations. This can make it difficult to troubleshoot issues, update systems, and keep pace with rapid advancements in the field.
  5. Integration with Existing Systems: Computer vision systems often need to interact with existing infrastructure, which can be difficult if existing systems are outdated or incompatible with new technologies.
  6. Reliability and Trust: For critical applications, computer vision systems must be extremely reliable. Moreover, humans must trust these systems, especially when they are used to automate tasks previously performed by humans. Building this trust requires time, clear communication, and demonstrable reliability.

While these challenges are significant, they are not insurmountable. With thoughtful planning, strategic investment, and ongoing efforts to develop and adapt technologies, CNI industries can successfully leverage computer vision to enhance safety, efficiency, and effectiveness.

Trends in Computer Vision

As we dive into the future, we can see several emergent trends in computer vision that are enhancing its capabilities and creating opportunities for novel applications.

  1. Real-Time Processing: Speed is of the essence in many computer vision applications. The ability to process and analyze visual data in real time is improving rapidly, leading to enhanced performance in applications like autonomous vehicles, real-time surveillance, and interactive AR/VR experiences.
  2. Edge Computing: As devices become more powerful, more computer vision processing is being done on the edge – that is, on the device itself rather than in a central server. This reduces latency, increases privacy, and allows for computer vision applications in environments with limited connectivity.
  3. Improved Accuracy: Thanks to advancements in machine learning and neural network architectures, computer vision systems are becoming more accurate. This is particularly true for challenging tasks like object detection in cluttered scenes or facial recognition under varying lighting conditions.
  4. Data Efficiency: Traditionally, training computer vision models required vast amounts of labeled data. However, with techniques like transfer learning, synthetic data, and semi-supervised learning, it’s becoming possible to train robust models with less data.
  5. Explainability and Fairness: As computer vision becomes more widely used, there’s an increasing focus on making models interpretable and fair. This involves developing methods to understand why a model makes a certain prediction, and ensuring that models do not propagate or amplify bias.

These trends are pushing the boundaries of what’s possible with computer vision and promise to drive its adoption even further in the years to come.

Conclusion: The Future of CNI Industries with Computer Vision

The application of Computer Vision in CNI Industries is transforming these sectors. From optimizing operations to enhancing safety, the impact of Computer Vision is significant.

The future looks promising. The global computer vision market is projected to reach a significant size of $58.29 billion by the year 2030. This projection is based on a Compound Annual Growth Rate (CAGR) of 19.6% from 2023 onwards.

With advancements in technology, the capabilities of Computer Vision will only improve. As it becomes more integrated into CNI Industries, it will continue to drive efficiency and safety, making it an indispensable part of these sectors.

However, the journey of artificial intelligence doesn’t end here. While computer vision allows machines to ‘see’ and understand the world, another facet of AI, Natural Language Processing (NLP), is enabling machines to ‘read’, ‘listen to’, and understand human language.

In our next blog post, we’ll delve into the fascinating world of NLP. We’ll explore how it’s changing the landscape of human-computer interaction, making technology more accessible, and creating new possibilities for automation and insights. So, stay tuned and get ready to dive deeper into the realm of artificial intelligence.


  1. DigiYatra: Future of Air Travel, a Contactless Experience (newdelhiairport.in)
  2. SmartGates | Dubai Airports
  3. Machine perception of three-dimensional solids (mit.edu)
  4. How drones are revolutionizing the telecom industry – Telecom Review
  5. jio drones: Jio using drones for tower surveillance, upkeep ahead of 5G rollouts – The Economic Times (indiatimes.com)
  6. https://techblog.comsoc.org/2023/01/31/sk-telecom-inspects-cell-towers-for-safety-using-drones-and-ai/
  7. https://iocl.com/NewsDetails/59280
  8. https://energy.economictimes.indiatimes.com/news/oil-and-gas/oil-india-launches-drone-surveillance-to-enhance-productivity/87295504
  9. Computer Vision Market Size To Reach $58.29Bn By 2030 (grandviewresearch.com)

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