Omkar Poshatwar - Academia.edu (original) (raw)
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Papers by Omkar Poshatwar
International Journal of Innovative Research in Technology, Jun 1, 2018
Convolutional neural networks (CNNs) are a type of layered deep neural network comprised of artif... more Convolutional neural networks (CNNs) are a type of layered deep neural network comprised of artificial neurons. These neurons are initially taught a set of rules and conditions, through training, which dictate whether they will fire when given varying inputs. CNNs learn as they are used and make future decisions based on both the taught and learned information. A common application of CNNs is object and feature recognition in images. The CNN identifies features in an image by analyzing data pixels through layers of neurons. This is particularly useful in the field of autonomous vehicles where CNNs can be used to process driving footage and identify possible obstacles. CNNs will often classify sections of the preset image grid that potentially contain an obstacle. Errors that occur are fed back into the network for reclassification and further learning. After the analysis is complete and a final conclusion has been reached, the CNN outputs a signal for the vehicle to perform an action: keep driving, stop, turn, etc.
International Journal of Innovative Research in Technology, Jun 1, 2018
Convolutional neural networks (CNNs) are a type of layered deep neural network comprised of artif... more Convolutional neural networks (CNNs) are a type of layered deep neural network comprised of artificial neurons. These neurons are initially taught a set of rules and conditions, through training, which dictate whether they will fire when given varying inputs. CNNs learn as they are used and make future decisions based on both the taught and learned information. A common application of CNNs is object and feature recognition in images. The CNN identifies features in an image by analyzing data pixels through layers of neurons. This is particularly useful in the field of autonomous vehicles where CNNs can be used to process driving footage and identify possible obstacles. CNNs will often classify sections of the preset image grid that potentially contain an obstacle. Errors that occur are fed back into the network for reclassification and further learning. After the analysis is complete and a final conclusion has been reached, the CNN outputs a signal for the vehicle to perform an action: keep driving, stop, turn, etc.