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.
