Traffc Signs Detection using YOLOv8: A Real-Time Deep Learning Approach for Autonomous Driving Systems

Abstract

Traffic Sign Detection and Recognition (TSDR)— the automatic localisation and classification of traffic signs from onboard cameras—is a fundamental component of Advanced Driver Assistance Systems (ADAS) and autonomous vehicles: approximately 1.3 million road fatalities occur annually worldwide, with driver inattention to traffic signs ranked the second most common contributing factor after speeding. However, existing TSDR approaches exhibit notable limitations: two-stage and transformer-based detectors are too slow for real-time onboard inference, anchor-based single-stage detectors require extensive anchor tuning and degrade sharply on small and distant signs, and most published systems neglect deployment readiness and region-specific sign diversity such as Indian signage. To overcome these problems, we propose a complete real-time TSDR pipeline built on YOLOv8, combining an anchor-free prediction head, a C2F backbone, and a Path Aggregation Network (PANet) neck, complemented by a multi-task CNN (EfficientNet-v2 with Online Hard Example Mining) for Indian traffic signs and ONNX export for edge deployment. In this paper, the pipeline is trained and evaluated end-to-end, and its results are positioned against a structured survey of the 2025–2026 state of the art (YOLO-BS, CPB-YOLOv8, FEBG-YOLOv8s, CRS-NET, and an optimised YOLOv7). Experimental results show that the proposed pipeline achieves Precision: 90.6%, Recall: 88.9%, and mAP@0.5: 93.1% (validation) / 92.3% (test) after only 30 epochs on the primary dataset; on the CCTSDB2021 benchmark, YOLOv8 attains mAP@0.5: 97.1% on the test split, outperforming both YOLOv7 and YOLOv9, while the multi-task CNN achieves state-of-the-art F1 = 98.0% on the Indian traffic sign dataset. The exported ONNX model retains full accuracy at 128 FPS on GPU, demonstrating a deployment-ready real-time system.