Computer vision for field use in agriculture and recycling w/Michael Nielsen

Michael talks about his work in computer vision for field use in agriculture and recycling. He started out in computer vision in the agriculture space doing machine vision and 3D reconstruction of plants. He then moved to the Danish Technological Institute when they expanded their work on machine vision for field use in agriculture.Michael worked with a fusion of sensors like stereo vision, thermography, radar, lidar and high frame rate cameras, merging multiple images for high dynamic range. All this to be able to navigate the tricky situation in a farm field where you need to navigate close to or even in what is grown. Multi-baseline cameras were also used to provide range detection over a wide range of distances.We also learn about how he expanded his work into sorting recycling, a very challenging problem. Here the sensor fusion gives him RGB as well as depth and temperature. Adding a powerful studio flash to the setup allowed him to heat the material being sorted, making it possible to determine the material, depending on how it absorbs the heat from the flash. Michael is also working on adding cameras capable of seeing above the human range of vision to make it easy to specify which materials to pick. We also hear about the problems faced when using time of flight and sheet of light cameras. He then shares some good results using stereo vision, especially combined with blue light random dot projectors.This podcast is part of the Wevolver network. Wevolver is a platform & community providing engineers informative content to help them innovate.Learn more at Wevolver.comPromote your company in our podcast?If you are interested in sponsoring the podcast, you can contact us at [email protected]