Recognition of High-Voltage Isolation Switch Opening and Closing State Based on Image Fusion

To solve the low recognition rate problem of the existing isolation switch state identification, a method of image fusion based on NSST-PCNN-IFVSS is proposed. Image registration is performed in the preprocessing stage of infrared and visible light images; subsequently, pixels and fusion are used to achieve the fusion of the two images. In the fusion stage, the non-subsampled shearlet transform is used to decompose the infrared and visible light images into high- and low-frequency sub-band images. In the high-frequency sub-band image part, a pulse coupled neural network is used for fusion, whereas the image fusion method based on visual saliency segmentation is used for fusion in the low-frequency sub-band image part. The two sub-band images are combined by the inverse transform of the non-subsampled shearlet transform to obtain the fused image. A fusion quality index evaluation scheme is established to compare the effect of this method with common image fusion methods. The fused image is processed by a pixel integration projection algorithm to determine the state of the high-voltage isolation switch. Experimental simulation verifies that the image fusion effect of the non-subsampled shearlet transform-pulse coupled neural network-image fusion based on visual salience segmentation is better than six common fusion methods, and the recognition result after image fusion is better than that of the single visible light image and infrared image.