A comparison of a Convolutional Neural Network and an Extreme Learning Machine for obscured traffic sign recognition

Drost, Folke (2018) A comparison of a Convolutional Neural Network and an Extreme Learning Machine for obscured traffic sign recognition. Bachelor's Thesis, Artificial Intelligence.

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Abstract

The present paper compares two neural network architectures for traffic sign recognition (TSR). First, a convolutional neural network (CNN) pre-trained for object recognition and retrained for TSR. Second, a single-hidden-layer feed forward neural network (SLFN), trained by an extreme learning machine (ELM) algorithm with a histogram of oriented gradient (HOG) feature extractor. The comparison focusses on recognition accuracy and computational costs regarding normal as well as obscured traffic signs. The models are trained and tested on a combination of traffic signs from the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) datasets. Results show an advantage of the ELM in recognition accuracy, computational costs and robustness on obscured traffic signs.

Item Type:	Thesis (Bachelor's Thesis)
Supervisor name:	Netten, S.M. van and Wolf, B.J.
Degree programme:	Artificial Intelligence
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	02 Jul 2018
Last Modified:	03 Jul 2018 14:20
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/17540

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