Thermal infrared object detection with Yolo models

Abstract

Object detection is a fundamental task in computer vision and remote sensing, aimed at recognizing and categorizing different types of objects within images. Unmanned aerial vehicle - based thermal infrared remote sensing provides crucial multi-scenario images and videos, serving as key data sources in publ icapplications. However, object detection in these images remains challenging due to complex scene information, lower resolution compared to visible-spectrum videos, and a shortage of publicly available labeled datasets and trained models. This article introduces a Unmanned aerial vehicle - based thermal infrared object detection framework for analyzing images and videos in public applications and evaluates the performance of YOLOv8n/v8s, YOLOv11n/v11s, and YOLOv12n/v12s models in extracting features from ground-based thermal infrared images and videos captured by Forward-Looking Infrared cameras, as well as from unmanned aerial vehicle - recorded thermal infrared videos taken from various angles. The YOLOv8n/v8s, YOLOv11n/v11s, and the latest YOLOv12n/v12s models were deployed on a Raspberry Pi 5 using the OpenVINO framework. The successful deployment of these models, including the most recent version, demonstrates their feasibility for unmanned aerial vehicle-based thermal infrared object detection. The results show that YOLOv8 and YOLOv11 achieved high accuracy and recall rates of 93% and 92%, respectively, while the YOLOv12 model demonstrated good precision but comparatively lower performance in accuracy and recall, suggesting the possibility for further improvement.