A Hierarchical Bayesian Fusion Method of Infrared and Visible Images for Temperature Monitoring of High-Speed Direct-Drive Blower

Li Wang, Puhan Zhao, Ning Chu, Liang Yu, Ali Mohammad-Djafari

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)18815-18830
页数16
期刊IEEE Sensors Journal
22
19
DOI
出版状态已出版 - 1 10月 2022
已对外发布

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