Convolutional Neural Network: does each filter in each convolution layer create a new image? Say I have a CNN with this structure:


input = 1 image (say, 30x30 RGB pixels)
first convolution layer = 10 5x5 convolution filters
second convolution layer = 5 3x3 convolution filters
one dense layer with 1 output


So a graph of the network will look like this:



Am I correct in thinking that the first convolution layer will create 10 new images, i.e. each filter creates a new intermediary 30x30 image (or 26x26 if I crop the border pixels that cannot be fully convoluted).

Then the second convolution layer, is that supposed to apply the 5 filters on all of the 10 images from the previous layer? So that would result in a total of 50 images after the second convolution layer.

And then finally the last FC layer will take all data from these 50 images and somehow combine it into one output value (e.g. the probability that the original input image was a cat).

Or am I mistaken in how convolution layers are supposed to operate? 

Also, how to deal with channels, in this case RGB? Can I consider this entire operation to be separate for all red, green and blue data? I.e. for one full RGB image, I essentially run the entire network three times, once for each color channel? Which would mean I'm also getting 3 output values.
About the images inside the CNN layers: I really recommend this article since there is no one short answer to this question and it probably will be better to experiment with it.

About the RGB input images: When needed to train on RGB pictures it is not advised to split the RGB channels, you can think of it by trying to identify a fictional cat with red ears, green body and blue tail. Each separated channel don't represent a cat, most certainly not with high confidence. I would recommend to transform you RGB images to gray scale and measure the network performance. If the performance are not sufficient you can make a 3D convolution layer. For example: If 30x30x3 is the input image, the filter has to be NxNx3.