VR Detection Technology for the Safety of Regenerative Continuous Heating Furnace Tubes under Feature Labeling
When conducting safety inspections on the furnace tubes of a regenerative continuous heating furnace,if the quality of the collected furnace tube damage images is poor,it will directly reduce the accuracy of subsequent safety inspections.In order to improve the accuracy of safety detection,a feature marked VR detection technology for the safety of regenerative continuous heating furnace tubes is proposed.This method first uses infrared thermal imaging technology to obtain infrared images of furnace tubes,and uses double-layer wavelet coefficients to denoise the images;After completing image denoising,use VR technology to reproduce the target of the denoised image;By combining differ-ential convolutional templates,label the temperature features of the furnace tube image and complete temperature feature labeling;Finally,u-sing temperature labels as model parameters,a safety detection model is established based on artificial neural networks to complete the safety status detection of the regenerative continuous heating furnace tubes.The experimental results show that when using this method for furnace tube safety detection,the infrared image processing effect of the furnace tube is good,and the safety detection accuracy and efficiency are both high.
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