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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Sharance, Randlin Paul
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Computer Science & IT

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Efficient traffic signal detection with tiny YOLOv4: enhancing road safety through computer vision Santhiya, Santhiya; Johnraja Jebadurai, Immanuel; Leelipushpam Paulraj, Getzi Jeba; Veemaraj, Ebenezer; Sharance, Randlin Paul; Keren, Rubee; Karan, Kiruba
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i2.pp285-296

Abstract

As decades go by, technology advances and everything around us becomes smarter, such as televisions, mobile phones, robots, and so on. Artificial intelligence (AI) is applied in these technologies where AI assists the computer in making judgments like humans, and this intelligence is artificially fed to the model. The self-driving technique is a developing technology. Autonomous driving has been a broad and fast-expanding technology over the last decade. This model is carried out using the tiny you only look once (YOLO) algorithm. YOLO is mainly used for object detection classification. Tiny YOLO model is explored for the traffic signal detection. ROBI FLOW dataset is used for object detection which contains 2000+ image data to train the tiny YOLO model for traffic signal detection in real time. This model gives an improved accuracy and lightweight implementation compared to other models. Tiny YOLO is fast and accurate model for real-time traffic signal detection.
Co-Authors Johnraja Jebadurai, Immanuel Karan, Kiruba Keren, Rubee Leelipushpam Paulraj, Getzi Jeba Santhiya, Santhiya Veemaraj, Ebenezer