SPONSORED: AI-ASSISTED IMAGING


Answering AI’s imaging issues


Gemma Church looks at how finding the right AI-assisted software can benefit specific vision tasks


T


he advent of artificial intelligence (AI) technology has helped multiple industries and the imaging and


machine vision market is no exception. AI-assisted imaging is currently


used in areas such as machine vision, manufacturing, agriculture and smart cities. Brandon Hunt, product manager at Teledyne Dalsa, explained: ‘Smart cities are an emerging application area as Industry 4.0 and 5G take charge. Te resulting IoT infrastructure starts to enable more data and connectivity than ever before. All this extra data is rocket fuel for AI models to perform well.’ From food packages to flat panel displays,


automotive parts and medical x-rays, AI- assisted inspection tools are now entering those markets where standard algorithms have challenges, from high variation rates to changes in shapes or lighting levels. Hunt defines these cases as ‘any area where the logic is fuzzy and requires a human’s judgement’. However, AI is still fairly new to the


imaging industry. Hunt explained: ‘Tere are still many companies out there who rely on humans for applications like defect inspection and they often are not even aware of what AI can do for them.’ As such, AI-assisted imaging still faces


challenges surrounding managing user expectations. ‘One of the biggest challenges is communicating what AI can or cannot do and explaining the process,’ Hunt continued. ‘Tere is often a gap between the technology and the customer’s expectations.’ AI models follow a different workflow, for example, which is iterative in nature. As a result, users must repeatedly run the workflow and analyse the results that are returned. ‘Some customers want specific levels of accuracy, but it’s hard to get that kind of information without trying it out


Different coloured licence plates


first,’ Hunt added. ‘Ten, if something goes wrong in the


AI model, it’s not a straightforward fix. It usually requires experimentation and trying out different parameters, datasets – and requires an engineer to look into those areas.’


Tis is where the right platform can help,


providing users with an intuitive interface and the tools to understand both how AI works and how it can benefit specific imaging applications.


Working together Te user experience is a key factor to increase adoption rates and help everyone understand the benefits of AI-assisted imaging tools. To achieve this, these tools must integrate


with existing, traditional image processing software. Tis provides users with the best of both worlds, allowing them to cater to their needs while also lowering the barrier to entry, streamlining the user experience and learning process.


22 IMAGING AND MACHINE VISION EUROPE APRIL/MAY 2022 Te latest developments in tools such as


Teledyne Dalsa’s Sapera Vision Software can provide field-proven image acquisition, control, image processing and artificial intelligence functions to help users design, develop and deploy high-performance machine vision applications. Sapera Vision Software includes Astrocyte, a GUI-based tool for training AI models. Users can, for example, use the GUI-based


Sherlock machine vision in conjunction with Astrocyte. Tis provides them with a no-code environment, democratising these AI models for everyone. Visualisation is another important means to help users understand the AI-assisted tools with which they are working. When dealing with anomaly detection, such data visualisations can help users intuitively understand any detected defects, presenting the size and location of those defects in an intuitive manner.


An anomaly detection algorithm can


robustly locate defects while generating output heatmaps. Te provision of


@imveurope | www.imveurope.com


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36