Thermal images have various applications in security, medical and industrial domains. This paper proposes a practical deep-learning approach for thermal image classification. Accurate and efficient classification of thermal images poses a significant challenge across various fields due to the complex image content and the scarcity of annotated datasets. This work uses a convolutional neural network (CNN) architecture, specifically ResNet-50 and VGGNet-19, to extract features from thermal images. This work also applied Kalman filter on thermal input images for image denoising. The experimental results demonstrate the effectiveness of the proposed approach in terms of accuracy and efficiency.

翻译：热成像在安防、医疗和工业领域具有广泛应用。本文提出了一种实用的深度学习方法用于热图像分类。由于图像内容复杂且标注数据集稀缺，准确高效的热图像分类在各领域面临重大挑战。本研究采用卷积神经网络（CNN）架构，具体使用ResNet-50和VGGNet-19，从热图像中提取特征。同时，本文对输入的热图像应用卡尔曼滤波器进行图像去噪。实验结果表明，所提方法在准确性和效率方面均具有有效性。