<![CDATA[This research aims to formulate and assess a real-time traffic sign detection framework in the context of Indonesia, using YOLOv11. Given the heterogeneous nature of traffic signs and road conditions in Indonesia, there is an urgent need for a robust and precise model to improve driving safety. The findings show that the model successfully achieved a Mean Average Precision (mAP) of 0.99, simultaneously demonstrating high accuracy across a wide range of traffic sign classifications. Evaluation using Confusion Matrix, shed light on the negligible error rate, signaling that the model has sufficient reliability for real-world applications. The potential applications of this technology are crucial in strengthening Indonesia's driving safety and intelligent transportation systems.]]>
Copyrights © 2024
