Deep Learning-Based Computer Vision
Just a few short years ago, computer vision was seen as something that held great potential but which wasn’t quite there yet. Today, thanks to advances in AI, more affordable GPU capacity and an accumulation of data and the means to train it, computer vision and ai applications are becoming strategic technology assets for companies in a variety of industries. In fact, according to a Forrester blog post , 58% of senior business purchase influencers said that their firms are implementing, planning to implement, or interested in implementing computer vision in the coming year.
This thinking was recently outlined in a Forrester research report entitled, All Enterprises Need (Computer) Vision, June 14, 2019 (access requires subscription or payment). In the report, Forrester describes the four most established use cases for computer vision, while also sharing the key questions to ask when planning your computer vision strategy. We at Wovenware were honored that we were cited as an example of a provider that creates labeled training datasets, which we consider the secret sauce to effective computer vision solutions.
While the common strategy of computer vision is to capture, process and analyze real world images and videos to uncover meaningful information, there are currently different ways to get there. Automated machine learning tools for creating computer vision apps are available that provide plug-and-play capabilities that make it possible for a basic programmer to pretty easily build a basic model. We’ve found, however, that these types of machine learning solutions might be a good way to begin, yet companies serious about leveraging computer vision for competitive advantage need custom solutions built from the ground up, around real-world images that are identified and labeled from scratch.
Custom deep learning -based computer vision and AI applications provide the deep-dive analysis that comes from understanding and honing in on unique business needs and specific customer behaviors. They leverage a deep understanding of customer behaviors and challenges – within specific industries – as well as experience into what resonates in certain markets, and how to transfer that knowledge to machines.
What’s important as well, for companies looking into implementing computer vision, is to consider that in order to be effective, it requires constant care and feeding – in the form of new data, images, video and other content. Computer vision can never be a one and done proposition.
Yet, as Forrester eloquently describes in the report, computer vision is allowing companies to collect unprecedented intelligence about the most important aspects of their businesses. It is enabling a whole new level of awareness, understanding and insights that can improve lives, making people safer, cities more efficient and health diagnoses more accurate. Consider the following examples of computer vision at work today.
- Transforming advertising. Companies, such as Gannett, are turning to deep learning and computer vision to design better online ads, determining which colors, images and fonts work best. The company says that this has boosted click-through rates across different news sites.
- Improving patient outcomes. Computer vision can help physicians diagnose diseases, among other applications. For example, a physician or radiologist can use it to review brain scans and determine healthy or not so healthy areas of the brain.
- Enabling safer autonomous driving. Deep learning-enabled computer vision is being applied in autonomous driving to navigate roads and make quick decisions in real time, such as identifying an oncoming vehicle or slowing down on icy pavement.
- Making shopping easier. In one example, cameras are being placed in the ceiling above aisles and on shelves in a brick & mortar retail location, and using computer vision technology these cameras can determine when an object is taken from a shelf and who has taken it. If an item is returned to the shelf, the system is also able to remove that item from a customer’s virtual basket. The network of cameras allows the app to track people in the store at all times, ensuring it bills the right items to the right shopper when they walk out, without having to use facial recognition.
Satellites Bring Computer Vision to a Whole New Level
When computer vision and ai applications are deployed in satellites, the possibilities are boundless. Consider the following:
- Tackling deforestation. Computer vision and deep learning can help detect the number or specific species of trees in certain forests and parks to determine their growth or risk, and if deforestation is occurring, it can help to address the specific factors that could be causing it.
- Tracking economic growth. By monitoring the numbers of cars, electric lights in the night sky, construction, we can track the development and economic growth of countries around the world.
- Responding to world crises. In situations such as a refugee crisis or war, satellite imagery can help provide valuable information that can be used to plan for the supply of life-sustaining resources like food and shelter materials.
Computer vision is gaining major traction in a variety of industries, providing an extra set of really smart eyes that can identify vulnerabilities to safety, identify anomalies in medical images and improve customer experience. Yet, companies need to take a strategic approach to its implementation, mapping out the most direct and efficient route to making the promise of computer vision a reality.
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