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In this example, both the availability of high quality data as well as the support of a highly skilled team of specialists were some of the key success factors for tackling the existing problem. By using satellite images paired with other geographic data, the improved solutions are able to accurately predict future illegal deforestation which enables early action. The success of this implementation rests on the fact that there was a clear business problem expressed by the need to process increasingly larger amounts of data as well as a highly specialized team that was able to develop the solution 6.Īnother great example is our work on preventing illegal deforestation 7, where we successfully improved and scaled existing solutions by integrating data pipelines and creating fully custom web applications. By automating drone flight routes around wind turbines, high quality and standardized image data was collected which was then used to identify potential inspection issues.
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Our approach entails starting from a clearly defined problem, making impact more measurable, as well collecting as much high quality data as possible by exploring different sources and modes of collection.Īs an example, a scalable cloud solution was built for a client in the renewable energy sector. AI’s transformational character requires targeted investment in upskilling and reskilling throughout the organization.ĭeloitte analytics leaders are making use of the above 3 factors to support the implementation of computer vision solutions capable of adding value to our clients’ organizations.For computer vision, in particular, this implies both quality footage, stressing the importance of hardware, as well as accurate and consistent labelling when training models. More data is not always better, but rather quality data is needed.Businesses should not engage with AI for the sake of AI but use it to solve a specific business problem.A recent study 5 supported by Nyenrode and Deloitte, finds that three factors emerge as cross-cutting for successful adoption of AI: This raises the question about the ideal approach that should be considered by analytics leaders to preserve the enthusiasm around the value of computer vision and AI in general. In fact, most companies that invest in computer vision struggle to bring proof of concepts into reality and derive tangible business value from their investments 4. While these successful and groundbreaking examples of computer vision applications showcase its ability to transform and disrupt industries, the challenges faced during the implementation process can potentially become crippling.
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Why successful computer vision implementations are challenging In this blog series, we will demonstrate how these challenges can be tackled in order to reap the tangible value from computer vision. Despite this, several implementation challenges remain, which can obscure some of its proven benefits. In this phase, real-world benefits of the technology are demonstrated and accepted and tools become increasingly stable. By now, computer vision has progressed through the Gartner Hype Cycle for Artificial Intelligence 2, and is one of the AI technologies closest to entering the “Plateau of Productivity”. This has been enabled by several technological advancements such as internet, (cloud) computing capabilities and the development of new (deep) neural networks.
GARTNER HYPE CYCLE FOR ARTIFICIAL INTELLIGENCE 2020 FULL
Modern day examples of such models include object detection, pose estimation, image classification and face recognition.Īlthough implementing computer vision in practice proved to be more difficult and resource-intensive than initially thought, in the recent decades we have started to see examples where it has shown its full potential. This branch of AI allows machines to extract meaningful information from image and video data by using sophisticated machine learning models. With the initial hype around Artificial Intelligence (AI) in the 1960s, computer vision was at the forefront of the optimism shown by researchers 1. A brief journey into the Computer Vision landscapeĪs a new technology emerges, so does the hype around its potential value.