Real-Time Traffic Sign Recognition Using YOLOv7: A Robust Deep Learning Approach for Autonomous Driving

Traffic Sign Recognition (TSR) is a critical component of autonomous driving systems and Advanced Driver Assistance Systems (ADAS). However, real-world environmental challenges such as occlusions, lighting variations, multi-scale changes, and motion blur often degrade the performance of traditional vision pipelines. This paper presents a robust real-time TSR system based on the YOLOv7 architecture. The proposed model leverages an E-ELAN backbone for hierarchical feature extraction, a PANet neck for multi-scale semantic fusion, and an anchor-free IDetect head for precise localization. Trained on a custom traffic_sign_data dataset with aggressive data augmentation, the system achieves 94% mAP@0.5 and 38% mAP@0.5:0.95 at 45 frames per second on an NVIDIA RTX 3090 GPU. Comparative evaluations show that YOLOv7 significantly outperforms YOLOv5 with 89% mAP@0.5 and Faster R-CNN with 91% mAP@0.5 at only 12 FPS. The model is further optimized via ONNX-to-TensorRT conversion, enabling efficient deployment on edge computing platforms such as NVIDIA Jetson AGX Xavier.