Model GHT-SVM for Traffic Sign Detection Using Support Vector Machine Algorithm Based On Gabor Filter and Top-Black Hat Transform

Handrie Noprisson; Vina Ayumi; Erwin  Dwika Putra; Marissa  Utami; Nur Ani

doi:10.23887/janapati.v13i3.75778

Authors

Handrie Noprisson Universitas Dian Nusantara
Vina Ayumi Universitas Dian Nusantara
Erwin Dwika Putra Computer Vision Research Laboratory, PT Towwar Tech Ind3
Marissa Utami Computer Vision Research Laboratory, PT Towwar Tech Ind3
Nur Ani Universiti Kebangsaan Malaysia

DOI:

https://doi.org/10.23887/janapati.v13i3.75778

Keywords:

Black Hat Transform, Top Hat Transform, Gabor Filter, Support Vector Machine, Traffic Signs

Abstract

A factor that can hinder the detection and recognition of traffic signs is the variation in lighting in the image of traffic signs. This study aims to detect traffic symbols using Gabor Filter (GFT), Top Hat Transform (THT), and Black Hat Transform (BHT) methods on the Support Vector Machine (SVM) algorithm for traffic sign dataset images with data problems that tend to have dark backgrounds at night and bright backgrounds during the day. From the experimental results, GHT-SVM gets the highest accuracy compared to HSV-SVM, HSV-RF, HSV-KNN, and H2T-SVM models. Based on experimental results, H2T-SVM from HOG ⊕ ENT ⊕ BHT ⊕ SVM results get the best accuracy of 86.42%. The Gabor Filter (GFT) parameters used are the number of filters with a value of 16, ksize with a value of 30, sigma with a standard deviation value of 3.0, lambd with a sinusoidal factor value of 10.0, gamma with a spatial aspect ratio value of 0.5 and psi with a phase offset value of 0 while the Top Hat Transform (THT) and Black Hat Transform (BHT) methods use filterSize sizes with values (3, 3).

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