Dash Yajnaseni, Gupta Vinayak, Abraham Ajith, Chandna Swati
School of Artificial Intelligence, Bennett University, Greater Noida 201310, India.
School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India.
J Imaging. 2025 Feb 24;11(3):69. doi: 10.3390/jimaging11030069.
The advancement of technology has ushered in remote sensing with the adoption of high-altitude infrared thermal object detection to leverage the distinct advantages of high-altitude platforms. These new technologies readily capture the thermal signatures of objects from an elevated point, generally unmanned aerial vehicles or drones, and thus allow for the enhancement of the detection and monitoring of extensive areas. This study explores the application of YOLOv8's advanced architecture, as well as dynamic magnitude-based pruning techniques paired with non-maximum suppression for high-altitude infrared thermal object detection using UAVs. The current research addresses the complexities of processing high-resolution thermal imagery, where traditional methods fall short. We converted dataset annotations from the COCO and PASCAL VOC formats to YOLO's required format, enabling efficient model training and inference. The results demonstrate the proposed architecture's superior speed and accuracy, effectively handling thermal signatures and object detection. Precision-recall metrics indicate robust performance, though some misclassification, particularly for persons, suggests areas for further refinement. This work highlights the advanced architecture of YOLOv8's potential in enhancing UAV-based thermal imaging applications, paving the way for more effective real-time object detection solutions.
随着高空红外热目标检测技术的采用，技术的进步带来了遥感技术，以利用高空平台的独特优势。这些新技术能够从高处，通常是无人机，轻松捕捉物体的热特征，从而增强对大面积区域的检测和监测能力。本研究探讨了YOLOv8先进架构的应用，以及结合基于动态幅度的剪枝技术和非极大值抑制，用于无人机的高空红外热目标检测。当前的研究解决了处理高分辨率热图像的复杂性问题，而传统方法在这方面存在不足。我们将COCO和PASCAL VOC格式的数据集标注转换为YOLO所需的格式，以实现高效的模型训练和推理。结果表明，所提出的架构具有卓越的速度和准确性，能够有效地处理热特征和目标检测。精确率-召回率指标显示出强大的性能，不过存在一些误分类情况，尤其是对人的误分类，这表明还有进一步改进的空间。这项工作突出了YOLOv8先进架构在增强基于无人机的热成像应用方面的潜力，为更有效的实时目标检测解决方案铺平了道路。
