Hold on a second here - are unlabeled pixels used in training a NN to do detection? Will a typical NN get trained to label those pixels as “not a human”? I agree that they should be labeled, but it’s the difference between needing to throw more data at the problem (because you aren’t getting as much learning per image as you could) and actively training the car to do something bad.
Yes, if you don’t “punish” incorrect predictions in your loss function your neural net could just get “perfect” accuracy by putting a giant bounding box around the entire image.
Technically that’s “right”; it did put a box around all the obstacles just like you asked it to. But that “solution” is not useful. You want it to find what it’s looking for and only what it’s looking for.
In this case, if it detects an unlabeled pedestrian the loss function will penalize it a bit for that “wrong” answer and it will slightly deviate to try to not find that pedestrian but still find the correctly labeled examples. It’s trying to fit the examples you give it best as possible.
