Deep Learning Based Traffic Sign Recognition Using YOLO Algorithm
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Accurate and fast detection of traffic signs will also eliminate an important technical problem in autonomous vehicles. With the developing artificial intelligency technology, deep learning applications can distinguish objects with high perception and accurate detection. New applications are being tested in this area for the detection of traffic signs using artificial intelligence technology. In this context, this article has an important place in correctly
detecting traffic signs with deep learning algorithms. In this study, three model of (You Only Look Once) YOLOv5, an up-to-date algorithm for detecting traffic signs, were used. A system that uses deep learning models to detect traffic signs is proposed. In the proposed study, real-time plate detection was also performed. When the precision, recall and mAP50 values of the models were compared, the highest results were obtained as 99.3, 95% and 98.1%, respectively. Experimental results have supported that YOLOv5 architectures are an accurate method
for object detection with both image and video. It has been seen that YOLOv5 algorithms are quite successful in detecting traffic signs and average precession.
Dr. Gökalp Çınarer is an Assistant Professor in the Department of Computer Engineering at Yozgat Bozok University. He completed his PhD in Computer Engineering at Kırıkkale University between 2017-2021 and wrote his thesis on "Detection of Brain Tumors with Image Processing Techniques and Analysis with Classification Algorithms".
His research interests focus on artificial intelligence, machine learning, image processing and deep neural networks. He has carried out important academic studies using artificial intelligence applications in different fields such as medicine, agriculture, food technologies and environmental analysis.
Since 2021, he has been continuing his administrative duties as the Head of the Department of Information Processing and Head of the Department of Computer Engineering Software at Yozgat Bozok University. He teaches undergraduate and graduate courses such as Machine Learning, Deep Learning, Python Programming, Algorithm Analysis, Artificial Intelligence Applications.
His academic publications include more than 75 articles scanned by SCI-Expanded, SSCI and Scopus, and international conference proceedings. His work covers various topics such as brain tumor analysis with artificial intelligence, artificial intelligence applications in agriculture, environmental data analysis and object detection.
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