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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST)
Riski, Agung Mustika
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Computer Science & IT

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Traffic Sign Detection Using Region And Corner Feature Extraction Method Hendra Maulana; Yudha Kartika, Dhian Satria; Riski, Agung Mustika; Nurlaili, Afina Lina
IJCONSIST JOURNALS Vol 3 No 1 (2021): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3139.835 KB) | DOI: 10.33005/ijconsist.v3i1.54

Abstract

Traffic signs are an important feature in providing safety information for drivers about road conditions. Recognition of traffic signs can reduce the burden on drivers remembering signs and improve safety. One solution that can reduce these violations is by building a system that can recognize traffic signs as reminders to motorists. The process applied to traffic sign detection is image processing. Image processing is an image processing and analysis process that involves a lot of visual perception. Traffic signs can be detected and recognized visually by using a camera as a medium for retrieving information from a traffic sign. The layout of different traffic signs can affect the identification process. Several studies related to the detection and recognition of traffic signs have been carried out before, one of the problems that arises is the difficulty in knowing the kinds of traffic signs. This study proposes a combination of region and corner point feature extraction methods. Based on the test results obtained an accuracy value of 76.2%, a precision of 67.3 and a recall value of 78.6.
Co-Authors Dhian Satria Yudha Kartika Maulana, Hendra Nurlaili, Afina Lina