ADVANCED REAL-TIME TRAFFIC SIGN SEGMENTATION AND CLASSIFICATION USING HYBRID DEEP CONVOLUTIONAL ARCHITECTURES ON GTSDB AND BENCHMARK DATASETS
Autonomous driving systems and Advanced Driver Assistance Systems (ADAS) heavily rely on precise visual recognition of traffic control infrastructure under varying real-world conditions. While traditional object detection methods utilize bounding boxes, pixel-level semantic and instance segmentation provide essential geometric detail required for motion planning and vehicle control. This paper presents a rigorous comparative analysis of deep learning models, specifically U-Net, Mask R-CNN, and YOLOv8-Seg, for real-time traffic sign segmentation. Training and evaluation were conducted on benchmark datasets including the German Traffic Sign Detection Benchmark (GTSDB) and TT100K. To address extreme pixel-level class imbalance inherent in roadside imagery, a hybrid compound loss function combining Focal Loss and Dice Loss was developed. Experimental results indicate that YOLOv8-Seg achieves an optimal trade-off with a mean Average Precision (mAP50) of 89.7% and Intersection over Union (IoU) of 81.3% while operating at 45.2 Frames Per Second (FPS) on standard embedded GPU hardware. U-Net demonstrated superior boundary reconstruction accuracy with an IoU of 82.1%, whereas Mask R-CNN achieved peak precision (91.2% mAP50) at reduced inference speeds (12.4 FPS). The source code implementation and optimized hyperparameter setup in PyTorch 2.0 are detailed.