Deep Learning Based Fusion of RGB and Infrared Images for the Detection of Abnormal Condition of Fused Magnesium Furnace

In fused magnesium furnace (FMF) process, semi-molten is one of the most harmful abnormal working conditions, under which the furnace wall is thinned by the overheated fused magnesium because of the uneven impurities in raw material. If the condition is not detected on its early stage, the furnace can be burnt through. At present, semi-molten is detected by experienced operators by directly “observing the fire” at the production site of FMF. There is a high risk and the labor intensity is high. Such practice of detection relied on human may cause safety issues and can lead to missed or false detection. This work introduces a detection technology for the semi-molten working condition of FMF based on the fusion of RGB images and infrared thermal images. The classifier is established using deep Convolutional Neural Network (CNN) model trained using historical data. Also, to tackle the problem of insufficient training data, the Deep Convolutional Generative Adversarial Networks (DCGAN) is employed to generate extra samples. Finally, industrial experiments carried out in a magnesium oxide plant to show the effectiveness of the technology.