ExplainYOLOv11: A Robust and Interpretable YOLOv11 Framework for Real-Time Traffic Sign Detection in Autonomous Vehicles

A new YOLOv11 traffic sign detection framework for autonomous driving and intelligent transportation systems (ITS) with an explanation is introduced. The proposed approach not only enhances the accuracy of the detection system but also incorporates the possibility of evaluating the robustness of the system and performing a computational efficiency analysis, along with the ability to explain why the system made the decision it did. The framework undergoes systematic testing and benchmarking against the existing YOLOv5 – YOLOv11 with the same experimental conditions and under challenging real-world conditions such as low-light, noise and occlusion. To increase transparency in safety-critical applications, the proposed model will include Grad-CAM and attention-based explanations. The YOLOv11 model that has been developed achieves excellent detection results with an mAP@0.5 of 0.989 and has been adeptly deployed through a real-time web-based deployment prototype, showcasing the model’s practicality.