Deep learning-assisted Common Temperature Measurement Based on Visible Light Imaging

Abstract

Real-time, contact-free temperature monitoring of low to medium range (30°C ~ 150 °C) has been extensively used in industry and agriculture, which is usually realized by costly infrared temperature detection methods. This paper proposes an alternative approach of extracting temperature information in real time from the visible light images of the monitoring target using a convolutional neural network (CNN). A mean square error of ＜ 1.119 °C was reached in the temperature measurements of low to medium range using the CNN and the visible light images. Imaging angle and imaging distance do not affect the temperature detection using visible optical images by the CNN. Moreover, the CNN has a certain illuminance generalization ability capable of detection temperature information from the images which were collected under different illuminance and were not used for training. Compared to the conventional machine learning algorithms mentioned in the recent literatures, this real-time, contact-free temperature measurement approach that does not require any further image processing operations facilitates temperature monitoring applications in the industrial and civil fields.