Convolutional Neural Network Based Thermal Image Classification

Abstract

Classification of Thermal Images has been extensively used for its significant applications in many fields. There are many problems with the visible spectrum like object shadows, clothes or the body of human being matches the background and different lighting conditions. These limitations are overcome by using thermal imaging. Each and every object emits heat (Infrared energy) according to its temperature. Normally the hotter object emits more radiation then the colder one. As all objects have mostly different temperature so thermal camera detects them and these objects get appear as distinct objects. In the start thermal imaging was used by military for detection, recognition and identification of enemy personnel and equipment. Now a days it is extensively used in detection of face, self-driving car, detection of pedestrian and it also have application in the field of environmental work that is monitoring for energy conservation and pollution control. This research presents a novel study for the classification of thermal images using convolutional neural networks (CNN). Research focused on developing a framework that detects multiple thermal objects using CNN. Developed a framework based on deep learning Inception v3 model; work with thermal images that are captured by Seek Thermal and FLIR. For training and testing of the model two datasets are used that include three classes’ cat, car, and man. For FLIR dataset the highest accuracy achieved is 98.91% and for Seek thermal dataset highest accuracy achieved is 100%. A comparison of proposed framework with some other CNN models (DenseNet, MobileNet and YOLOv4), with customized CNN model and with a conventional model is also presented. The results of proposed framework and comparison with other models prove that proposed framework is effective for the classification of thermal images.