Embedded Vision Improves High-End Surveillance of Critical Infrastructure

Vision systems have been a critical component of high-end surveillance and security systems for decades. Over time, the technology has evolved dramatically, and now embedded vision systems are making their way into security applications for entirely new functionality and performance. 

Like many industries, embedded vision promises to create major disruption within the surveillance and security market by enabling more efficient products than ever before. The latest embedded vision systems are creating new applications and new opportunities for growth, particularly for the surveillance of critical infrastructure.

The Challenges of High-End Surveillance of Critical Infrastructure

High-end surveillance of critical infrastructure is a major challenge for vision systems – they have to be able to brave all the elements and capture high quality images and streams in a variety of lighting and visibility scenarios, all over a very large area. Even the best vision systems aren’t perfect when it comes to surveillance of large infrastructure. 

Whether it’s oil fields, pipelines, transportation goods, refineries, or open mines, the challenges are similar. Often, broken equipment or pipelines can go unnoticed for long stretches of time, leading to significant financial losses. Any vision system more capable of capturing these incidents or even preventing losses will provide enormous return on investment (ROI). 

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Embedded Vision for the Surveillance of Large, Critical Infrastructure

Embedded vision systems are helping to solve the most fundamental challenges of surveilling large infrastructure. Smart cameras are increasingly being deployed in security applications because of their ability to capture, process, and analyze images at the edge of the system. Just like in inspection tasks, when an anomaly is detected, a signal to take action is sent.

The data collected by smart cameras can also be sent to a host computer and then to the cloud for processing, including training artificial intelligence (AI) models. These AI or deep learning models can then better recognize anomalies over time, increasing the productivity and reliability of the system as a whole. 

Embedded vision is playing an important role in the evolution of high-end surveillance applications, especially for the monitoring of large, critical infrastructure. New edge processing and deep learning techniques, in part enabled by embedded vision, allow for more robust surveillance features than ever before.