Imagine a world where machines see as well as – or better than – humans, catching safety hazards in mines, spotting defects on a factory line, or flagging abnormalities in X-rays. That’s where AI-powered computer vision is already taking mining, manufacturing, and healthcare – driving real gains in safety, efficiency, and costs. In this blog, we’ll walk through computer vision’s evolution. We’ll explore why ResNet was a turning point, how easy it is to fine-tune models like YOLO, and look at a few practical applications that save time – and lives. Let’s start at the beginning.
A Brief History of Computer Vision
Back in the 1960s, computer vision was rudimentary – think basic pattern recognition for simple shapes. Fast forward to the 2010s, and deep learning has changed everything. Convolutional neural networks (CNNs) gave machines the ability to analyze complex images, like spotting a hard hat or a tumor. By 2015, these models hit an inflection point, surpassing human accuracy in tasks like object detection. This leap opened doors to real-world applications, from ensuring miner safety to speeding up medical diagnoses, making computer vision a practical tool for industries like yours.
Why ResNet Was a Game-Changer
In 2015, ResNet (short for Residual Network) changed everything. Its clever skip connections let neural networks go deeper without losing accuracy, solving a problem called vanishing gradients. Before ResNet, piling on layers often made models worse, not better. ResNet’s breakthrough meant computers could reliably spot intricate patterns – like a cracked loader tooth or a misaligned car part, and all in real time. This laid the groundwork for models like YOLO, powering high-stakes tasks in mining, manufacturing, and healthcare with precision humans couldn’t match.
The Ease of Fine-Tuning YOLO Models
YOLO, or You Only Look Once, is a breakthrough in its own right. Open source toolkits like Ultralytics’ YOLO11 make fine-tuning straightforward, allowing businesses to adapt models for specific tasks like detecting PPE in mining or tumors in X-rays without needing deep coding expertise. With user-friendly interfaces and pre-built modules, you can train on your own datasets quickly, which means faster deployment in environments where every second counts.
Practical Use Cases for Computer Vision
Computer vision isn’t theory, it’s solving real problems across industries as we speak. Here are a few examples that really stand out to me because they’re tied to tangible benefits –
In mining, it significantly boosts safety and output. For instance, models can detect if workers are wearing proper PPE like helmets and vests, or standing in unsafe exclusion zones while heavy machinery is operating – reducing safety incidents. They can also monitor ore crushers for material buildup, reducing the duration of downtime relating to blockages that cause lower yield. Another key application: spotting broken teeth from front-end loaders that end up on conveyor belts, avoiding costly equipment damage and further downtime.
Healthcare sees huge wins too. Analyzing X-rays and CT scans helps flag abnormalities, cutting misdiagnosis rates and ensuring conditions don’t slip through. Video search adds another layer: sifting through hundreds of hours of footage to find incidents like blocked emergency exits and identifying repeat offenders. Then there’s Harrison.ai, an Australian startup whose AI algorithms detect up to 124 conditions on Chest X-rays and are available in over 40 countries.
In manufacturing, it’s about quality and cost savings. Inspecting paint spray on vehicles right in the factory catches issues early, far cheaper than fixing them after the car has made it to the destination country fully assembled.
PLCs and Computer Vision in Manufacturing
Programmable logic controllers (PLCs) from vendors like Rockwell Automation and Siemens are starting to weave in basic computer vision, especially in operational technology (OT) networks controlling heavy machinery in factories and mine sites. These can handle simple tasks like checking bottle fill levels or spotting damaged labels on high-speed lines. The tight integration at the edge means minimal latency, keeping pace with fast-moving production. But while handy, this is basic stuff – nowhere near as sophisticated as add-ons like Aervision, again from Australia, who are supported by Intel’s ISV incubation program. The business brings advanced AI to a wide variety of use cases without being locked to PLCs.
Framework for Deploying Computer Vision Models
Deciding where to run your model (edge, cloud, or hybrid) comes down to two big factors: where the data lives and your latency needs.
First, data location. In disconnected spots like remote mine sites or hospitals that need to continue operating when a link is down, edge processing makes sense. The same goes for locations that might have hundreds of cameras storing data on a traditional CCTV system – like in a Casino, or Sports Stadium. For petabyte-scale radiology archives in hospitals, moving data to the cloud is too slow and impractical. This is where Cisco’s purpose-built servers enable fast, reliable on-premises edge inference capable of handling these workloads.
On the other hand, let’s say hypothetically you are a gas station or Starbucks, and you want to use cameras on your drive-thru to get insights to optimize lattes served per hour to improve throughput. You might have under 10 cameras per site, great bandwidth and cameras that stream their feeds to the cloud – so it would make sense to do all your AI post processing in the cloud where the data is stored. Cisco Meraki cameras come with built-in AI inference chips, enabling intelligence at the edge. When paired with SaaS solutions like Cogniac (a Cisco Ventures investment), deployment becomes seamless: simply draw bounding boxes around the objects you want to recognize, and Cogniac auto-deploys the AI model directly to the Meraki camera – making setup fast, easy, and turnkey.
Latency is the other key here. For time-sensitive apps, like a fast-moving conveyor belt for example, edge is essential. A satellite uplink round-trip to the cloud might delay a stop signal, letting the belt move two meters and miss the opportunity to remove a foreign object.
Enhancing Existing CCTV Deployments
Casinos, mine sites, and hospitals often rely on traditional CCTV systems from providers like Milestone, Genetec, or Avigilon. While effective for standard surveillance, these systems typically fall short on advanced AI. That’s where add-on software comes in – enhancing existing cameras with edge inference capabilities without requiring a costly rip-and-replace. Here, solutions like Aervision (an Australian startup in Intel’s ISV program) and MeldCX bring features like real-time safety monitoring and smoke detection.
Breakthroughs That Mean Business
From its early days to ResNet’s 2015 breakthrough and YOLO’s ease today, computer vision has grown into a critical tool for industries like mining, manufacturing, and healthcare. With flexible deployment models (edge for speed and data control, cloud for simplicity) plus integrations like PLCs and AI add-ons, it’s now within reach for almost any organization.
Whether you’re modernizing existing CCTV or starting fresh, Cisco Meraki with Cogniac, or Cisco servers running Aervision or MeldCX, make advanced vision both practical and powerful. To me, it’s no longer a question of whether to implement tools like this, but how they’re best applied to your business.
