Abstract: During driving, drivers often overlook the traffic signs along the roads compromising road safety and increasing the risk of accidents. To address this, artificial intelligence (AI) and deep learning techniques are employed, taking into consideration the improvement of advances in Artificial Neural Networks (ANNs) and image processing for robust road sign detection. In this work, we compare the performance of existing state-of-the-art object detection models for road sign detection, including YOLOv8, YOLOv9, RTMDet, Faster-RCNN and RetinaNet, using a large dataset of images of road signs. These models are fine-tuned and hyperparameters are optimized with varied settings like auto-orientation and augmentation during the preprocessing and training phase. The models are then tested, and key performance indicators such as mean average precision (mAP), number of inferences performed per second [frames per second (fps)], and total loss are evaluated. Our study reaffirms the earlier findings in which YOLOv9 and YOLOv8 outperform other detectors in real-time detection tasks because they are faster in inference or prediction than most detectors, but with a compromise in accuracy, as highlighted by the fast fps rates. In contrast, RTMDet is fast and reliable, making it a highly effective option for detecting various road signs. The insights presented in this research are useful in identifying the appropriateness and drawbacks of each model, thereby benefiting from the selection of the best suited model for real-world applications, such as autonomous vehicles or self-driving cars.

Zainab Fatima, M. Hassan Tanveer, Hira Mariam, Razvan Cristian Voicu, Tanazzah Rehman and Rizwan Riaz, “Performance Comparison of Object Detection Models for Road Sign Detection Under Different Conditions” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151299

@article{Fatima2024,
title = {Performance Comparison of Object Detection Models for Road Sign Detection Under Different Conditions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151299},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151299},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Zainab Fatima and M. Hassan Tanveer and Hira Mariam and Razvan Cristian Voicu and Tanazzah Rehman and Rizwan Riaz}
}