Open AccessReal-Time Object Recognition using Region based Convolution Neural Network and Recursive Neural Network
Author(s) -
Dr.R. Priyatharshini*,
Adlin Jebakumari S,
R. Sundar,
Gaud Nirmal
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8326.118419
Subject(s) - computer science , artificial intelligence , convolutional neural network , convolution (computer science) , recurrent neural network , cognitive neuroscience of visual object recognition , pattern recognition (psychology) , artificial neural network , feature (linguistics) , deep learning , object (grammar) , neocognitron , time delay neural network , key (lock) , image (mathematics) , machine learning , feature extraction , transfer of learning , kernel (algebra) , mathematics , linguistics , philosophy , computer security , combinatorics
The recognition of real-world objects demands the recognition and characterization of digital image samples. Automated methods for the detection and recognition of entity types have many significant commercial and industrial applications. While deep convolution neural networks (CNN) and machine learning (ML) concepts have contributed to the classification of globe items, they cannot fully scale the reliance of powerful GPUs to classify the key attributes of images. By using a Recurrent Neural Network (RNN) we tend to resolve the issue arisen in the previous systems. In particular, a hybrid approach using R-CNN and RNN has been proposed that improve the accuracy of object recognition and learn structured image attributes and begin image analysis. Specifically, we applied the transfer learning approach to pass the load parameters which were pre-trained on the Image web dataset to the RNN portion and follow a custom loss feature for the model to train and test more rapidly with precise weight parameters. Experimental results show that in comparison to CNN models like Resent, origin V3, etc., our proposed model achieved improved accuracy in categorizing universe pictures.