Test-object recognition in thermal images

Abstract

The paper presents a comparative analysis of several methods for recognition of test-object position in a thermal image when setting and testing characteristics of thermal image channels in an automated mode. We consider methods of image recognition based on the correlation image comparison, Viola-Jones method, LeNet classificatory convolutional neural network, GoogleNet (Inception v.1) classificatory convolutional neural network, and a deep-learning-based convolutional neural network of Single-Shot Multibox Detector (SSD) VGG16 type. The best performance is reached via using the deep-learning-based convolutional neural network of the VGG16-type. The main advantages of this method include robustness to variations in the test object size; high values of accuracy and recall parameters; and doing without additional methods for RoI (region of interest) localization.

Keywords

• image classification
• object detection in images
• image recognition
• deep-learning-based convolutional neural network
• thermal image
• thermal imaging device