In this paper, we present a synthetic thermal imaging dataset for Person Detection in Intrusion Warning Systems (PDIWS). The dataset consists of a training set with 2000 images and a test set with 500 images. Each image is synthesized by compounding a subject (intruder) with a background using the modified Poisson image editing method. There are a total of 50 different backgrounds and nearly 1000 subjects divided into five classes according to five human poses: creeping, crawling, stooping, climbing and other. The presence of the intruder will be confirmed if the first four poses are detected. Advanced object detection algorithms have been implemented with this dataset and give relatively satisfactory results, with the highest mAP values of 95.5% and 90.9% for IoU of 0.5 and 0.75 respectively. The dataset is freely published online for research purposes at https://github.com/thuan-researcher/Intruder-Thermal-Dataset.

翻译：本文提出了一个面向入侵预警系统中行人检测的合成热成像数据集（PDIWS）。该数据集包含2000张训练图像和500张测试图像。每张图像均通过改进的泊松图像编辑方法合成，将主体（入侵者）与背景进行融合。数据集涵盖50种不同背景及近1000个主体，根据人体姿态分为五类：匍匐、爬行、弯腰、攀爬及其他。若检测到前四种姿态，则确认存在入侵者。基于该数据集，先进目标检测算法取得了较满意的结果，在IoU为0.5和0.75时的最高平均精度（mAP）分别达到95.5%和90.9%。该数据集已在GitHub（https://github.com/thuan-researcher/Intruder-Thermal-Dataset）上免费公开，供研究使用。