基于梯度图像融合的高压隔离开关状态识别OA

In high-voltage disconnector state recognition,a single image modality generally provides limited information,resulting in low recognition accuracy.To address this problem,a gradient-based image fusion method was proposed to fuse the infrared and visible images of high-voltage disconnectors,followed by state recognition using a lightweight MobileNetV2 network.First,the infrared and visible images were decomposed into high-and low-frequency subband images using the non-subsampled shearlet transform(NSST).The high-frequency subband images were then fused using the multi-scale morphological gradient(MSMG)and weighted local energy(WLE),whereas the low-frequency subband images were fused using the guided filter(GF)and sum of modified Laplacian(SML),thereby achieving local fusion.Subsequently,the fused high-and low-frequency subband images were reconstructed using inverse NSST to obtain the final fused image and achieve global fusion.Finally,a lightweight MobileNetV2 network was employed to recognize the states of high-voltage disconnectors.Experimental results showed that the proposed method achieved an average recognition accuracy of 93.9%,significantly outperforming several commonly used methods.