Research Article Open Access

Object Detection and Classification from Thermal Images Using Region based Convolutional Neural Network

Usha Mittal1, Sonal Srivastava1 and Priyanka Chawla1
  • 1 Lovely Professional University, India
Journal of Computer Science
Volume 15 No. 7, 2019, 961-971

DOI: https://doi.org/10.3844/jcssp.2019.961.971

Submitted On: 4 April 2019 Published On: 18 July 2019

How to Cite: Mittal, U., Srivastava, S. & Chawla, P. (2019). Object Detection and Classification from Thermal Images Using Region based Convolutional Neural Network. Journal of Computer Science, 15(7), 961-971. https://doi.org/10.3844/jcssp.2019.961.971

Abstract

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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Keywords

  • Object Detection
  • Classification
  • Faster R-CNN
  • Thermal Images
  • Visible Spectrum Images