State Classification of Cooking Objects Using a VGG CNN

Abstract

In machine learning, it is very important for a robot to know the state of an object and recognize particular desired states. This is an image classification problem that can be solved using a convolutional neural network. In this paper, we will discuss the use of a VGG convolutional neural network to recognize those states of cooking objects. We will discuss the uses of activation functions, optimizers, data augmentation, layer additions, and other different versions of architectures. The results of this paper will be used to identify alternatives to the VGG convolutional neural network to improve accuracy.

Convolutional neural network for maize leaf disease image classification

“…CNN is a method in deep learning that can perform various tasks such as image classification [2,3], segmentation [4,5], recognition [6,7], and objects detection [8,9]. CNN technology has grown widely including fields of medical image [10,11], autonomous drivers [12,13], robotics [14,15], and agricultural image [16]. Many image studies have been carried out, such as disease classification in 15 food crops using 5 convolutional layers [17], classification of diseases in 9 class plant images using googleNet [18].…”