Vehicle Classification Through Detection and Color Segmentation of Registration Plates Running on Raspberry Pi 3 Model B
Louielito Ferrolino1, Allysa Kate Brillantes2, Melvin Cabatuan3, John Anthony Jose4, Elmer Dadios5
1Louielito Ferrolino, Department of Electronics and Communications Engineering, De La Salle University, Phillipness.
2Allysa Kate Brillantes, Department of Electronics Engineering Technological University, Phillipness.
3Melvin Cabatuan, Department of Electronics and Communications Engineering, Cebu Institute of Technology, Cebu, Philippines.
4John Anthony Jose, Department of Electronics and Communications Engineering (ECE), IEEE Philippines.
5Sabrina Ahmad, Department of Electrical and Electronics Engineers, the Founder and Chair of IEEE Computational Intelligence Society, Phils.
Manuscript received on 20 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1298-1303 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10570882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1057.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classification of license plates provides useful information regarding the nature of the vehicle, whether it is used for public transport, a privately-owned vehicle, an official vehicle, or a special vehicle. In the Philippines, the registration plates of vehicles are classified by colors. Colors such as red, green blue, black, yellow are used to identify what vehicle classification the plate belongs to. The information is useful to applications in statistics and transport regulation. This paper discusses a convolutional neural network based embedded system that runs on Raspberry Pi 3 Model B. The said system provides a process of classifying vehicles using plate detection using Convolutional Neural Networks and color thresholding of registration plates using the RGB color space. TensorFlow and OpenCV libraries were utilized for the detection and classification.
Keywords: Convolutional Neural Network, License Plate Detection, Raspberry Pi 3, Vehicle Classification.
Scope of the Article: Classification