The study of deep learning for automotive logo recognition and classification
DOI:
https://doi.org/10.21533/pen.v11.i3.151Abstract
Most vehicle manufacturer recognition (VMR) techniques are established in vehicle logo recog-nition because a vehicle's logo is the most obvious sign from the vehicle's manufacturer. Howev-er, due to the difficulty in accurately segmenting a vehicle logo on the picture with demand in resilience against many imaging scenarios, logo recognition can still be challenging. After sub-jective overview about this scope, a convolutional neural network (CNN) method for VMR is investigated in this research, which does away with the need for exact logo detection and seg-mentation. A powerful pertaining approach has also been developed to improve real-world appli-cations to lower the high computational cost for kernel training on CNN-based systems. The con-tribution of this paper is to study the multiclass logo employing random forest ensemble learn-ing and convolution mapping in nonlinear space. To boost accuracy by roughly 35%, 800 images from 15 types of car classes were investigated in the paper.
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