And welcome back in this section, we'll take a look at non maximum suppression, which is a really
good technique that can be used to clean up our bounding box proposals.
So let's get started.
So firstly, a very common issue that happens with all Americans at the doctors is an issue of overlapping
bounding boxes like this.
We have one car.
Yet the object detector model predicts three of four boxes that overlap on the same object.
That's a problem because if you were trying to do object counting, it's going to mess up your results,
wouldn't it?
So non depression is a really simple and effective technique to go from this to this.
So let's see how it works.
So firstly, you collect all the overlapping bounding boxes here, which are these three boxes here?
Now we get the maximum probabilities associated with each box.
That's a probability of the box belonging to a class.
So imagine these are the probabilities here you can see from this diagram.
So we have the initial box being here.
Point it in the next box being point five and one point six.
So we select the box with the highest confidence score, and this confidence score is basically the
confidence that the classifiers and the object detector us making to see that this class is a call or
whatever class we have in mind.
So we add the one with the highest probability score here to something called the final proposal.
This which is initially empty.
And then we remove that box from the initial proposal list, which was the initial set of overlapping
boxes that we see here.
Then what we do, we calculate the IOU for the boxes in the initial proposal list, with the box in
the final proposal list.
So this green box here we calculate the IOU for 2.5 blocks over the green box and the point six box
over the green box.
Well, that gives us is a level of overlap.
So just a way to measure how much those boxes overlap.
So if you threshold, which is typically point five, but you can just as easily, if it's over that
threshold, then we reject it because what's happening there in step five is that we're seeing these
boxes overlap too much.
So we should reject this and we basically do this for all the proposal, all the proposals in the initial
list here.
And that's how we end up with nice clean up objects back to a model upwards like this.
So that's basically it for non non maximum suppression.
It gets us NIMH non maximum suppression because all of the boxes that are not the maximum confidence
score are basically suppressed.
So it's basically it's self-explanatory name.
So we'll stop there.
And now I'll start giving you a tour of all the different object detector families starting first with
our CNN's, which also includes Fast or CNN's at a faster RC and ends.
So I'll see you in the next section.
Thank you for watching.