Performance Comparison of YOLOv8 and YOLOv10 for Traffic Light Detection on a Jetson Nano Board

Detecting traffic lights is crucial for the development of advanced driver-assistance systems (ADAS). Accurate traffic light detection allows for timely responses to changing traffic conditions, thereby enhancing road safety and reducing accidents. However, achieving high detection accuracy with small model size and fast inference times poses significant challenges, particularly for deployment on resource-constrained devices. In this study, we perform a detailed performance comparison of YOLOv8 and YOLOv10 nano models for traffic light detection (TLD), specifically targeting deployment on an NVIDIA Jetson Nano board. The evaluation focuses on key metrics including mean average precision (mAP), inference speed, and computational efficiency under real-time constraints. YOLOv8 demonstrated slightly superior mAP, indicating better detection accuracy. In contrast, YOLOv10 exhibited faster inference speeds due to its architectural optimizations. This comparison underscores the trade-offs between model complexity and deployment feasibility in embedded systems, providing insights for selecting the appropriate model for TLD applications in resource-constrained environments. Further research is needed to explore additional datasets, particularly those containing traffic lights at night, and to apply quantized models on smaller edge devices.