International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 17, No. 3, 8 Jun. 2025
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Traffic Sign Detection and Recognition Using Yolo Models
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Author(s)
Mareeswari V.
1
Vijayan R.
1,*
Shajith Nisthar
1
Rahul Bala Krishnan
1
Index Terms
Traffic Sign Detection, YOLOv8, Road Safety, Intelligent Transportation, Deep Learning, Object Recognition, Driver Assistance
Abstract
With the proliferation of advanced driver assistance systems and continued advances in autonomous vehicle technology, there is a need for accurate, real-time methods of identifying and interpreting traffic signs. The importance of traffic sign detection can't be overstated, as it plays a pivotal role in improving road safety and traffic management. This proposed work suggests a unique real-time traffic sign detection and recognition approach using the YOLOv8 algorithm. Utilizing the integrated webcams of personal computers and laptops, we capture live traffic scenes and train our model using a meticulously curated dataset from Roboflow. Through extensive training, our YOLOv8 version achieves an excellent accuracy rate of 94% compared to YOLOV7 at 90.1% and YOLOv5 at 81.3%, ensuring reliable detection and recognition across various environmental conditions. Additionally, this proposed work introduces an auditory alert feature that notifies the driver with a voice alert upon detecting traffic signs, enhancing driver awareness and safety. Through rigorous experimentation and evaluation, we validate the effectiveness of our approach, highlighting the importance of utilizing available hardware resources to deploy traffic sign detection systems with minimal infrastructure requirements. Our findings underscore the robustness of YOLOv8 in handling challenging traffic sign recognition tasks, paving the way for widespread adoption of intelligent transportation technologies and fostering the introduction of safer and more efficient road networks. In this paper, we compare the unique model of YOLO with YOLOv5, YOLOv7, and YOLOv8, and find that YOLOv8 outperforms its predecessors, YOLOv7 and YOLOv5, in traffic sign detection with an excellent overall mean average precision of 0.945. Notably, it demonstrates advanced precision and recall, especially in essential sign classes like "No overtaking" and "Stop," making it the favored preference for accurate and dependable traffic sign detection tasks.
Cite This Paper
Mareeswari V., Vijayan R., Shajith Nisthar, Rahul Bala Krishnan, "Traffic Sign Detection and Recognition Using Yolo Models", International Journal of Information Technology and Computer Science(IJITCS), Vol.17, No.3, pp.13-25, 2025. DOI:10.5815/ijitcs.2025.03.02
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