An infrared-optical image registration method for industrial blower monitoring based on contour-shape descriptors

Abstract

Industrial blowers are energy-efficient and widely used, but fault monitoring is challenging. Infrared and visible light sensors monitor their status, capturing temperature distribution and time evolution in images and videos. Due to the complexity of the industrial scene and the large difference between the characteristics of infrared and visible images, the existing registration methods are unable to accurately align the infrared and visible images. This paper proposes a Neural Network (NN) based registration method of infrared and visible images for industrial blowers using contours and Weight Global Shape Context Descriptor (W-GSCD), by considering the distance between different feature points as weights in our feature registration method which improves the accuracy of the registration of IR and visible images compared to classical image registration methods. Experiments on real data show that the proposed method effectively addresses registration challenges from complex shapes and heterogeneous backgrounds, achieving promising results on the blower dataset.