Improved recognition rate of different material category using convolutional neural networks

The recognition a material quality is considered as a process of finding out the constituent material present in an object and it is regarded as a vital part in various applications. Hence, it is considered as a valuable approach for the creation of a system that possesses the ability to achieve recognition of a material. In this paper, we develop a mechanism using convolutional neural networks (CNNs) for material recognition. The CNN model initially trains itself with the features extracted from the image samples. Finally, the classification is carried out with CNN model that learn the classes obtained via CNN of different category of materials. The experimental validation is conducted to test the accuracy of CNN classifiers against various deep learning classifiers. The results on various materials show that the proposed CNN classifier obtains improved recognition accuracy than other methods.