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Addee
Addee

Reputation: 671

Why we need CNN for the Object Detection?

I want to ask one general question that nowadays Deep learning specially Convolutional Neural Network (CNN) has been used in every field. Sometimes it is not necessary to use CNN for the problem but the researchers are using and following the trend.

So for the Object Detection problem, is it a kind of problem where CNN is really needed to solve the detection problem?

Upvotes: 0

Views: 560

Answers (2)

Richard Løvehjerte
Richard Løvehjerte

Reputation: 594

A key concept of CNN's is the idea of translational invariance. In short, using a convolutional kernel on an image allows the machine to learn a set of weights for a specific feature (an edge, or a much more detailed object, depending on the layering of the network) and apply it across the entire image. Consider detecting a cat in an image. If we designed some set of weights that allowed the learner to recognize a cat, we would like those weights to be the same no matter where the cat is in the image! So we would "assign" a layer in the convolutional kernel to detecting cats, and then convolve over the entire image.

Whatever the reason for the recent successes of CNN's, it should be noted that regular fully-connected ANN's should perform just as well. The problem is that they quickly become computationally infeasible on larger images, whereas CNN's are much more efficient due to parameter sharing.

Upvotes: 1

viceriel
viceriel

Reputation: 873

That is unhappy question. In title you ask about CNN, but you ask about deep learning in general.

So we don't necessary need deep learning for object recognition. But trained deep networks gets better results. Companies like Google and others are thankful for every % of better results.

About CNN, they gets better results than "traditional" ANN and also have less parameters because of weights sharing. CNN also allow transfer learning(you take a feature detector- convolution and pooling layers and than you connect on feature detector yours full connected layers).

Upvotes: 1

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