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 imagesRead more