Mar 28 2023

ISC West 2023: How Computer Vision Increases Security and Improves Facility Operations

At the Port of Vancouver, extracting metadata from shipping containers in real time reduces inspection delays and keeps supply chains moving.

The Port of Vancouver is Canada’s largest seaport — as big as the next five Canadian seaports combined — and is the third largest in North America. During the pandemic, it was struggling to move shipping containers expeditiously through the massive facility, which includes 29 terminals, while also maintaining security.

The process of flagging containers for inspection and then inspecting them was slow and inefficient. It was causing grumbling from shippers, clogging the port with goods destined for the marketplace and costing money.

“It was a major source of complaints and challenges for the entire port community,” Greg Rogge, director of land operations and security at the Port of Vancouver, says in a case study. “Reliable information on actual container examination operations was limited, many processes were manual and information was distributed across multiple organizations.”

Containers could not be inspected on the spot; instead they had to be moved to secure facility, then returned. “Imagine coming into an international airport and being told that your bag has to be inspected,” said Quang Trinh, a systems and solutions architect for Axis Communications. “And then they tell you, ‘Oh, to inspect this bag, we actually have to put it on a bus, drive it to an off another facility, then we’ll get it back to you.’”

That’s not unlike what the container inspection process was like in Vancouver, Trinh explained, except that shippers would often also be hit with costs of up to $50,000 just for the inspection, even when no violations were found.

Trinh was speaking at ISC West, the leading comprehensive and converged security trade event in the U.S. The event continues through March 31 in Las Vegas.

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How the Port of Vancouver Used Computer Vision to Boost Efficiency

Enter real-time computer vision, a technology that adds machine learning processes to live and recorded video to produce metadata. The Port of Vancouver, using mainly its existing infrastructure of Axis security cameras plus AWS Panorama technology to perform the processing, was able use computer vision to add key metadata on containers in real time as they entered and moved through port facilities.

“It had been a very manual process in terms of how all this was being recorded and reported,” Trinh explained. Computer vision adds real-time metadata to the captured images, including each container’s identification number and its color, which often indicates what types of goods are inside the container.

The technology is helping the port develop a new class of insights on physical operations that will improve collaboration with shippers.

After applying computer vision, the Port of Vancouver was able to optimize its ground operations and expedite container inspections. According to Deloitte, which worked on the project at the port, “a bot predicts and sends alerts about berth allocation, container sequencing, and containers ready to return,” which allows port space to be used more efficiently and reduces the amount of time containers are held up.

DIG DEEPER: Discover how enhanced video surveillance provides better security.

Increased Efficiency With Computer Vision Cuts Expenses

The application of computer vision is also reducing costs by streamlining inefficient and manual container examination processes. “Anything from missing paperwork and intensive exams can delay shipments from a few days to a month,” Deloitte notes. “Some require additional labor for unloading and reloading cargo. They interrupt traffic flow and ground operations and increase storage and labor costs.”

Trinh said the project in Vancouver is a good example of the kind of machine learning-enabled computer vision that will become more common as organizations take advantage of the real-time insights they can glean, both for security and operational purposes, from metadata extraction.

“Deep learning with computer vision is here,” Trinh said, “and it’s going to advance object detection n and classification. What you’re going to see is this moving to a very proactive, because as we get really good and identifying and classifying vehicles and people, you can start to extrapolate other metadata from that. And that’s where you’ll start to get things like very forensic forensic search.”

Keep this page bookmarked for articles from the event, and follow us on Twitter at @BizTechMagazine and the official conference Twitter feed, @ISCEvents.

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