Object Detection and Classification from Thermal Images Using Region based Convolutional Neural Network
- 1 Lovely Professional University, India
Copyright: © 2020 Usha Mittal, Sonal Srivastava and Priyanka Chawla. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
In recent years, object detection and classification has gained so much popularity in different application areas like face detection, self- driving cars, pedestrian detection, security surveillance systems etc. The traditional detection methods like background subtraction, Gaussian Mixture Model (GMM), Support Vector Machine (SVM) have certain drawbacks like overlapping of objects, distortion due to smoke, fog, lightening conditions etc. In this paper, thermal images are used as thermal cameras capture the image by using the heat generated by the objects. Thermal camera images are not influenced by smoke and bad weather conditions which makes them a built-up apparatus in inquiry and safeguards or fire-fighting applications. These days, deep learning techniques are extensively used for detection and classification. In this paper, a comparative analysis has been done by applying Faster region based convolutional neural network on thermal images and visual spectrum images. The experimental results show that thermal camera images are better as compared to visible spectrum images.
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- Object Detection
- Faster R-CNN
- Thermal Images
- Visible Spectrum Images