Person detection in thermal images: A comparative analysis of YOLOv8 and YOLOv9 models

Abstract

Person detection in thermal imagery is crucial for surveillance and monitoring applications. We assess the performance of YOLOv8 and YOLOv9 models using a new thermal image dataset. Our study reveals that both models achieve high precision, with YOLOv8 showing a superior training time and inference speed compared to YOLOv9. Specifically, YOLOv8 models achieve precision rates of 89% to 90 %, outperforming YOLOv9 with precision ranging from 87% to 88%. Moreover, YOLOv8 models demonstrate faster training times and lower in-ference processing times, making them more suitable for real-time applications. Despite challenges such as false positives and false negatives, our findings provide valuable insights into improving the accuracy and efficiency of thermal-based person detection, thereby enhancing surveillance and monitoring systems.