“Optimizing Image Quality and Stereo Depth at the Edge,” a Presentation from John Deere

Travis Davis, Delivery Manager in the Automation and Autonomy Core, and Tarik Loukili, Technical Lead for Automation and Autonomy Applications, both of John Deere, present the “Reinventing Smart Cities with Computer Vision” tutorial at the May 2023 Embedded Vision Summit.

John Deere uses machine learning and computer vision (including stereo vision) for challenging outdoor applications such as obstacle detection, vision-based guidance and weed management, among many others. The quality of the images the company’s systems obtain, and the accuracy of the depth information produced by its stereo cameras, significantly impact the performance of the overall solutions.

In this talk, Davis and Loukili share some of the challenges John Deere has faced in developing image quality improvement and stereo vision algorithms. Many of the techniques found in academic research and prior work cannot be easily implemented in real-time applications at the edge and are difficult to scale for applications with varying performance and cost requirements. They highlight some of the alternative techniques their company has developed to provide optimized image-quality and stereo vision implementations that meet the requirements of John Deere’s product range. Stated another way, they share how they do more with less.

See here for a PDF of the slides.

Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.



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