Design Thinking Approach

Case Study Image and Video Processing

Client engaged Whirldata to explore innovative and cost effective options for technology spend

  • Studied the upper bounds of hyper spectral imaging and recommended against further R&D send on it
  • Deep learning neural networks were investigated and a cost effective solution was formulated after identifying appropriate learning networks
  • A pilot for detection was built to prove that specific deep learning networks were the path to invest further R&D spends on


Computer Vision & Hyperspectral Imaging (to detect plastic in a garbage line) failed to yield results.


A deep learning neural network (SegNet) provided acceptable results from low quality images

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