One of the most important problems in computer vision and remote sensing is
object detection, which identifies particular categories of diverse things in
pictures. Two crucial data sources for public security are the thermal infrared
(TIR) remote sensing multi-scenario photos and videos produced by unmanned
aerial vehicles (UAVs). Due to the small scale of the target, complex scene
information, low resolution relative to the viewable videos, and dearth of
publicly available labeled datasets and training models, their object detection
procedure is still difficult. A UAV TIR object detection framework for pictures
and videos is suggested in this study. The Forward-looking Infrared (FLIR)
cameras used to gather ground-based TIR photos and videos are used to create
the ``You Only Look Once'' (YOLO) model, which is based on CNN architecture.
Results indicated that in the validating task, detecting human object had an
average precision at IOU (Intersection over Union) = 0.5, which was 72.5\%,
using YOLOv7 (YOLO version 7) state of the art model \cite{1}, while the
detection speed around 161 frames per second (FPS/second). The usefulness of
the YOLO architecture is demonstrated in the application, which evaluates the
cross-detection performance of people in UAV TIR videos under a YOLOv7 model in
terms of the various UAVs' observation angles. The qualitative and quantitative
evaluation of object detection from TIR pictures and videos using deep-learning
models is supported favorably by this work.

本研究提出了一种适用于无人机热红外图片和视频的目标检测框架，基于 CNN 架构的 YOLO 模型，通过定量和定性评估深度学习模型，在人的交叉检测性能方面展示了 YOLOv7 模型在不同无人机观察角度下的应用。