Original article was published by /u/timmur_ on Deep Learning
I’m hoping to get some direction on a problem I’d like to try to solve using deep learning. The problem is a machine vision problem. I need to detect anomalous spray patterns in a spray drying application.
Specifically, the spray dryer has multiple spray nozzles that atomize the product (concentrated milk) before it is introduced into a hot air stream. Occasionally, a build-up occurs on the nozzle body causing an anonymous spray pattern. The real danger here is the build-up, which has been termed "bearding". The "beard" can smolder and drop off of the nozzle and cause a dryer explosion. To help monitor and prevent this, the spray dryer is equipped with cameras that allow an operator to spot the issue and take action.
My hope is to utilize the camera footage to train a deep network to recognize an abnormal spray pattern and alert the operator (or take action). I have a PC equipped with an Nvidia 2080 GTX video card and is running the latest version of Ubuntu. The PC can be set up with various deep learning platforms, but my thought was to use Keras as well as TensorFlow and/or MxNet and whatever else is required to conduct training.
I should mention that I’m almost quite inexperienced in deep learning. I’ve read a couple of books, followed along on some exercises, and tried a few small projects. With all of that said, I was wondering if this approach, "Anomaly Detection in Videos using LSTM Convolutional Autoencoder" will work for this application? Here’s the GitHub link to the same. My thought is that this is not a difficult problem compared to other machine vision problems, but not sure what other more experienced practitioners think? Thanks in advance for any thoughts, comments, suggestions, etc…