I’m testing out a part detection model out on edge impulse on a Raspberry PI 3b. I’ve tested a primitive model from edge impulse on the raspberry pi and runs fine. I’m now trying to optimize the model to detect when a part has been created on a manufacturing machine. To do this im reading the power the machine is using while creating the part, a successful part will look like the picture below, 2 sets of 4 peaks evenly spaced out.
im having issues detecting full parts over half parts. The reason im feeding half parts into the training algorithm is train the model to exclude anything that isn’t a full part. An example of a half part is below, just 5 peaks rather than 2 sets of 4 peaks.
I’m not sure which algorithms would be best suited to this type of Job, so far I’ve gone for Spectral Analysis, neural network and also K-means Anomaly Detection. I was hoping the k means would help pick up any outliers as shown in the pic, maybe it will be better with additional tweaks.
The validation results are pretty good. I’m getting 100% on everything part from the half parts, see picture below :
If anyone has any tips to make the model more accurate, it would be appreciated. I’ve made the project public and can be found here : https://studio.edgeimpulse.com/public/18358/latest
The other weird thing is on the neural network, the quantized 8bit predictions are really low but on the 32bit its high. Its not a problem, because im running it on a 32bit raspberry pi processor, just thought it was worth pointing out.