COMPUTER VISION

Preventing incidents in a mining environment involves many moving parts. First there’s the sensory overload: Drilling and ore hauling operations are loud and underground operations are often dimly lit. Different kinds of vehicles move around at varying speeds, there are no traffic lights, and it’s difficult to gain a comprehensive view of the surroundings. Coupled with long hours on constantly changing shifts, the conditions are ripe for worker safety to be compromised.

Fortunately, the mining industry can address the challenge in hazardous environments, whether above or below ground, with computer vision AI solutions. Kelvin Aongola, CEO and Founder of LabelFuse, a no-code platform for machine learning and computer vision solutions, says incident prevention is a high priority in the mining industry, but there is no standardized way of approaching the challenge.

“Companies are looking for cost-effective ways to address the problem,” Aongola says. Our Advanced Driver Assistance System (ADAS)—designed for the mining and long-distance trucking sectors—essentially serves as an incident prevention platform that leverages existing CCTV cameras to capture an accurate picture of driver fatigue and working conditions on the ground.

Computer Vision AI Detects Fatigue

In the mining use case, the environment is very loud with large vehicles surrounded by smaller ones. “If you’re all the way on top as a driver, your view can be completely blocked,” Aongola says. Accident prevention involves a number of traditional methods just to keep the driver awake.

The computer vision solution captures visual cues of tiredness—droopy eyes, blinking—that might be easy for humans to miss, and sends prompts to the driver. The program also places the driver in context, understanding what’s happening in the environment around the vehicle to better predict the possibility of an adverse outcome. “We also stream these activities to a control center so if the driver has ignored all alerts, then the control center can take charge,” Aongola says. The data can also help verify insurance claims in case of incidents.

Given that the AI algorithms scan the human face for signs of fatigue and distraction, privacy concerns understandably surface. But LabelFuse follows data privacy legislation and does not store personal data on the cloud where chances of compromise might be higher, Aongola says. The company also stores only metadata on-prem for not more than a few months.

While incident prevention is the current use case in the mining industry, the LabelFuse solution is equipped to lift a bigger load, Aongola says. The system can work well with ADAS and expand to autonomous driving use cases in the future. “There are possibilities to go beyond what we’re offering with the current setup,” Aongola says.

“While incident prevention is the current use case in the #mining industry, the LabelFuse solution is equipped to lift a bigger load” – Kelvin Aogola, LabelFuse via @insightdottech

The Desire for No-Code Solutions

Companies that don’t have the right AI expertise struggle with implementation. “If you see how computer vision is deployed, especially at the edge, most companies do a small proof of concept, but they are challenged to scale it up as a production-ready solution,” Aongola says. “They either struggle with fine-tuning their models or in figuring out how to use the right edge device to deploy their ideas.”

Enterprises that want to deploy AI-driven solutions are keen to work with no-code solutions so they can focus on their primary value proposition without becoming AI-first companies. No-code solutions democratize access to software because they enable even those without specialized programming skills to develop workable solutions for problems. Pre-built components and drag-and-drop functionality enable professionals to build capabilities without getting mired deep in programming fundamentals.

LabelFuse fills this need through its no-code platform that allows domain experts to simply log in and pick a model specific to a business’s operational needs.

The Intel Advantage for Edge Computing

LabelFuse relies on Intel technology for a number of reasons, including a reasonable cost. “When you’re speaking to your client, it’s easier to close that deal because the price point doesn’t require them to go through a complicated approval process; they can make a decision right then and there,” Aongola says.

Storing data in the cloud is challenging, so high-powered edge processing helps cut costs and latency. Powered by 13th Generation Intel^® Core^™ processors, the Intel^® NUC delivers all the performant compute needed. The device’s compact form factor and easy installation make it a great fit for vehicles with tight spaces. And the NUC can be placed in a ruggedized enclosure for mining’s harsh environments. The well-recognized brand name is another significant plus factor, Aongola says, as the “technology has been validated, you’re not using a no-name device to help solve a problem.”

Wider Adoption of Computer Vision AI

Although LabelFuse has found ready implementation of its incident prevention platform in mining, use cases extend beyond the sector. Any industry where worker attention might flag due to busy environments such as manufacturing, field services, or retail, can benefit from these computer vision AI solutions.

The way computer vision works is changing, Aongola says. People want solutions you can talk to, like ChatGPT equivalents for visual data. LabelFuse integrates such generative AI into edge offerings and already sees significant traction in that domain.

This article was edited by Georganne Benesch, Editorial Director for insight.tech.