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All Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Erwin Dwika Putra
Computer Vision Research Laboratory, PT Towwar Tech Ind3
Computer Science & IT Education Engineering

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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
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 13 No. 3 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i3.75778

Abstract

A factor that can hinder the detection and recognition of traffic signs is the variation in lighting in the image of traffic signs kamislot.  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).
Co-Authors Marissa Utami Noprisson, Handrie Nur Ani Vina Ayumi