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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Leelipushpam Paulraj, Getzi Jeba
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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.
Explainable artificial intelligence for traffic signal detection using LIME algorithm Santhiya, P.; Jebadurai, Immanuel Johnraja; Leelipushpam Paulraj, Getzi Jeba; Kirubakaran S, Stewart; Keren L., Rubee; Veemaraj, Ebenezer; Sharance J. S., Randlin Paul
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v13i3.pp527-536

Abstract

As technology progresses, so does everything around us, such as televisions, mobile phones, and robots, which grow wiser. Of these technologies, artificial intelligence (AI) is used to aid the computer in making decisions comparable to humans, and this intelligence is supplied to the machine as a model. As AI deals with the concept of Black-Box, the model’s decisions were poorly comprehended by the end users. Explainable AI (XAI) is where humans can understand the judgments and decisions made by the AI. Earlier, the predictions made by the AI were not as easy as we know the data now, and there was some confusion regarding the predictions made by the AI. The intention for the use of XAI is to improve the user interface of products and services by helping them trust the decisions made by AI. The machine learning (ML) model White-box shows us the result that can be understood by the people in that domain, wherein the end users cannot understand the decisions. To further enhance traffic signal detection using XAI, the concept called local interpretable model- agnostic explanation (LIME) algorithm has been taken into consideration and the performance is improved in this paper.
Co-Authors Jebadurai, Immanuel Johnraja Johnraja Jebadurai, Immanuel Karan, Kiruba Keren L., Rubee Keren, Rubee Kirubakaran S, Stewart Santhiya, P. Santhiya, Santhiya Sharance J. S., Randlin Paul Sharance, Randlin Paul Veemaraj, Ebenezer