Illustration of the image processing capabilities of convolution neural networks through prototype implementations

Abstract

Convolution neural network is a deep learning algorithm which is prominently applicable for image processing applications. The high feature learning capacity of convolution neural networks make it beneficial for applications involving image processing. Based on the learned features CNN network can easily classify data. This paper describes the capabilities of CNN network through three implementations. The first implementation on uses convolution neural networks for plant leaves disease detection. The second implementation uses CNN based implementation for the railway track’s crack detection. An underwater fish species classification implementation also discussed. These implementations show how efficiently CNN can perform the task in comparison with manual counterparts.