Machine vision may sound like the technology in a futuristic sci-fi movie. In fact, machine vision is already deployed in more advanced areas of technology all around us, woven into the operations of major industries — from transportation and logistics to food and pharmaceuticals.
The rapid growth in this field is largely fueled by strong demand from the automotive and food and beverage sectors. Those industries see machine learning as an integral part of their future toolkits for such applications as navigation, manufacturing, quality assurance, service confirmation, and passenger, pedestrian and driver safety.
But what exactly is machine vision? And how does it affect fleet operations?
“Simply put, machine vision uses image and video data to analyse and make predictions,” said Stephen Krotosky, manager of applied machine learning at Lytx.
At a basic level, machine vision could be harnessed to detect objects, such as mobile phones, safety cones, or hardhats. It could also help detect the presence of passengers, or even the lack of passengers to gauge occupancy in buses or other transport vehicles.
It does this by going through a “training” process where the system is fed hundreds of thousands of images that have been manually annotated by humans describing what each image contains. The system gradually “learns” what a mobile phone or a cigarette looks like so it can accurately detect those items in new images.
On a more advanced level, machine vision algorithms can be “taught” to better interpret what it sees, as well as what it doesn’t directly see, using artificial intelligence to take a step further to draw conclusions or calculate risk.
“Typically, this approach means making use of the latest advances in deep learning,” Krotosky.
But a machine vision model is only as smart as the data used to train it. To create an accurate model that can deal with all the variations that the real world can throw at it, you would need millions of unique images with all the situations imaginable. Lytx draws from a cache of images of more than 100 billion miles driven over the past 20 years in dozens of vehicle types, across the world, in all weather conditions, and over numerous road types. For Lytx’s researchers this rich cache of image data gives them the best available raw material to create machine vision models that are specifically tailored to commercial vehicle travel.
As anyone in the business knows, however, the vast majority of miles driven by commercial fleets is safe and free of incidents. The trick is in being able to recognise the critical moments that generate risk—the brief phone call, the five minutes taken to eat a sandwich or smoke a cigarette, the driver who fails to fasten their seatbelt. Catching those moments accurately is a lot like looking for a needle in a haystack. Machine vision is about sifting through mountains of data to find those brief but critical moments of risk.