Optimization of vehicle classification model using genetic algorithm

Authors

Cyril Dale L. Cero, De La Salle University, Manila
Edwin Sybingco, De La Salle University, Manila
Allysa Kate M. Brillantes, De La Salle University, Manila
Mari Christine E. Amon, De La Salle University, Manila
John Carlo V. Puno, De La Salle University, Manila
Robert Kerwin C. Billones, De La Salle University, Manila
Elmer P. Dadios, De La Salle University, Manila
Argel A. Bandala, De La Salle University, Manila

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019

Publication Date

11-1-2019

Abstract

This paper focuses on classifying vehicle types into car, van, motorcycle, bus, light truck, multi-axle truck and determine its class based on the Philippine Toll Regulatory Board's vehicle classification. This study utilized DEvol, an open-source tool that uses genetic algorithm for evolving number of filters and nodes, optimizer, activation, dropout rate. The model attained the best accuracy with 78.53% using 9000 images from MIO-TCD dataset. © 2019 IEEE.

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Digitial Object Identifier (DOI)

10.1109/HNICEM48295.2019.9072880

Recommended Citation

Cero, C. L., Sybingco, E., Brillantes, A. M., Amon, M. E., Puno, J. V., Billones, R. C., Dadios, E. P., & Bandala, A. A. (2019). Optimization of vehicle classification model using genetic algorithm. 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019 https://doi.org/10.1109/HNICEM48295.2019.9072880

Disciplines

Electrical and Computer Engineering

Keywords

Vehicles—Classification; Genetic algorithms; Neural networks (Computer science)

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