TY - JOUR
T1 - A Hierarchical Bayesian Fusion Method of Infrared and Visible Images for Temperature Monitoring of High-Speed Direct-Drive Blower
AU - Wang, Li
AU - Zhao, Puhan
AU - Chu, Ning
AU - Yu, Liang
AU - Mohammad-Djafari, Ali
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2022/10/1
Y1 - 2022/10/1
N2 - The high-speed direct-drive blower is a high-efficiency, energy-saving, and environmentally friendly blower, and it is widely applied in industrial fields. The temperature changes significantly affect the performance of the high-speed direct-drive blower, and it is crucial to monitor its temperature. Infrared and visible light cameras are simultaneously used to measure and characterize temperature information. The measurement method can have both the temperature information of the infrared image and the contrast information of the structure and contour of the visible light image. This article establishes an image fusion representation and probabilistic generation model of infrared and visible images. Then, the information of the infrared and visible images is fused under the Bayesian framework. A hierarchical prior model using the Haar wavelet transform is proposed. The joint maximum a posteriori criterion is chosen, and an appropriate alternate optimization algorithm is designed to achieve information fusion. The proposed method is validated in industrial scenarios of high-speed direct-drive blowers. The experimental results demonstrate the robustness and effectiveness of the proposed method.
AB - The high-speed direct-drive blower is a high-efficiency, energy-saving, and environmentally friendly blower, and it is widely applied in industrial fields. The temperature changes significantly affect the performance of the high-speed direct-drive blower, and it is crucial to monitor its temperature. Infrared and visible light cameras are simultaneously used to measure and characterize temperature information. The measurement method can have both the temperature information of the infrared image and the contrast information of the structure and contour of the visible light image. This article establishes an image fusion representation and probabilistic generation model of infrared and visible images. Then, the information of the infrared and visible images is fused under the Bayesian framework. A hierarchical prior model using the Haar wavelet transform is proposed. The joint maximum a posteriori criterion is chosen, and an appropriate alternate optimization algorithm is designed to achieve information fusion. The proposed method is validated in industrial scenarios of high-speed direct-drive blowers. The experimental results demonstrate the robustness and effectiveness of the proposed method.
KW - Bayesian fusion
KW - high-speed direct-drive blower
KW - infrared and visible images
KW - joint maximum a posteriori (JMAP) algorithm
UR - http://www.scopus.com/inward/record.url?scp=85137921375&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2022.3201226
DO - 10.1109/JSEN.2022.3201226
M3 - 文章
AN - SCOPUS:85137921375
SN - 1530-437X
VL - 22
SP - 18815
EP - 18830
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 19
ER -