I tried to applied sensor fusion to garbage classification :
I have (in different projects) :
- a collection of measure of the electrical capacity of different objects (a collection of labeled time series - *.csv files)
- a collection of the sound emitted by the object when it falls into the container ( a collection of labeled time series - *.wav files)
- a collection of images of objects ( a collection of labeled *.jpg files).
My idea is to “combine” these 3 features (the image, the sound and the electrical capacity) to determine the nature of an object (paper, plastic, metal, glass, cardboard).
my idea is to use 3 “preprocess blocks” to generate the feature associated to these 3 variables and then feed an unique neural network (NN classifier) with all the generated features.
I read the tutorial “Smoke detection” but after reading I have difficulties to adapt these use case to my own use case.
Can you help me to achieve this task (if it is feasible with Edge Impulse…) ?
All constructive comments will be warmly welcomed…!
Regards from France,