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Machine vision may sound like the technology in a futuristic sci-fi movie, like HAL in \u201c2001: A Space Odyssey\u201d or \u201cAuto,\u201d the artificial intelligence autopilot in Pixar Animation Studio\u2019s \u201cWALL-E.\u201d<\/p>\n
In fact, machine vision is already deployed in more advanced areas of technology all around us, woven into the operations of major industries \u2014 from transportation and logistics to food and pharmaceuticals. Lytx, for example, gives you the option to detect lane departure, following distance, rolling stops, and critical distance, among other things.<\/p>\n
Already, the market for machine vision technology is roughly $8.8 billion in 2017 and is set to surpass $18.2 billion annually by 2025, according to estimates from Grand View Research. The rapid growth is largely fueled by strong demand from the automotive and food & 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.<\/p>\n
\u201cSimply put, machine vision uses image and video data to analyze and make predictions,\u201d said Stephen Krotosky, manager of applied machine learning at Lytx.<\/p>\n
At a basic level, machine vision could be harnessed to detect objects, such as cell 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.<\/p>\n It does this by going through a \u201ctraining\u201d 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 \u201clearns\u201d what a cell phone or a cigarette looks like so it can accurately detect those items in new images.<\/p>\n On a more advanced level, machine vision algorithms can be \u201ctaught\u201d to better interpret what it sees, as well as what it doesn\u2019t directly see, using artificial intelligence to take a step further to draw conclusions or calculate risk.<\/p>\n \u201cTypically, this approach means making use of the latest advances in deep learning<\/a><\/u>,\u201d Krotosky.<\/p>\n 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\u2019d 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 all 50 states, in all weather conditions, and over numerous road types. For Lytx\u2019s researchers this rich cache of image data gives them the best available raw material to create machine vision models that\u2019s specifically tailored to commercial vehicle travel.<\/p>\n As anyone in the business knows, however, the vast majority of miles driven by the nation\u2019s commercial fleet is safe and free of incidents. The trick is in being able to recognize the critical moments that generate risk\u2014the brief phone call, the five minutes taken to eat a sandwich or smoke a cigarette, the driver who fails to fasten their seatbelt.\u00a0Catching 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. That neat trick, called anomaly detection, is\u00a0the subject of our next article<\/a> in the series on Machine Vision and Artificial Intelligence.<\/p>\n\u00a0Stephen Krotosky – manager of applied machine learning, Lytx<\/small><\/cite><\/p>\n
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