Please use this identifier to cite or link to this item:
Title: Fusion based En-FEC Transfer Learning Approach for Automobile Parts Recognition System
Authors: Prasad, Mukesh
Rajora, Shantanu
Deepak, Gupta
Daraghmi, Yousef-Awwad
Daraghmi, Eman
Pranay, Yadav
Prayag, Tiwari
Amit, Saxena
Keywords: Feature extraction , Computer architecture , Engines , Training , Task analysis , Convolution , Neural networks
Issue Date: 2019
Publisher: IEEE
Citation: M. Prasad et al., "Fusion based En-FEC Transfer Learning Approach for Automobile Parts Recognition System," 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 2018, pp. 2193-2199. doi: 10.1109/SSCI.2018.8628789 keywords: {convolutional neural nets;engines;image classification;learning (artificial intelligence);mechanical engineering computing;pattern classification;implicated paradigms;subjected data;classifier;deep learning approach;operational engine;crank shafts;air duct;automobiles;convolution neural networks;typical classification problem;robust transfer learning paradigm;correct class label;test images;conclusive result;corresponding class label;computationally intelligent architecture;labeled training data;automobile parts recognition system;artificially supervised classification;world entities;phenomenal significance;computational advancements;intelligent classification model;ConvNet architecture;fusion based En-FEC transfer learning approach;machinery-engine part;rock-arms;assecorybelt;Feature extraction;Computer architecture;Engines;Training;Task analysis;Convolution;Neural networks;Deep learning;convolution neural networks;transfer learning;image classification}, URL:
Abstract: The artificially supervised classification of real world entities have gained a phenomenal significance in recent year of computational advancements. An intelligent classification model focuses on rendering accurate outcomes vide the implicated paradigms with respect to the subjected data employed to train the classifier. This paper proposes a novel deep learning approach to classify the various parts of any operational engine such as crank shafts, rock-arms, distributer, air duct, assecorybelt etc. deployed in automobiles. The proposed architecture distinctively utilizes convolution neural networks for this typical classification problem and altogether constructs a robust transfer learning paradigm to render the correct class label against the validation and test images as the conclusive result of the classification. The proposed methodology poses in such a way that it can qualitatively classify and henceforth give the corresponding class label of the machinery/engine part under consideration. This computationally intelligent architecture requires the user to feed the image of the engine part to the model in order to achieve the requisite responses of classification. The main contribution of the proposed method is the development of a robust algorithm that can exhibit pronounced results without training the entire ConvNet architecture from scratch, thereby enabling the proposed paradigm to be deployable in application instances wherein limited labeled training data is available.
Appears in Collections:Applied science faculty

Files in This Item:
File Description SizeFormat 
fusion.png1.21 MBimage/pngThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.