Swin Transformer-based detection and identification of defects in power grid equipment
Traditional image processing algorithms are slow in recognition and poor in detection of the images of grid equipment in complex environments.Based on an improved Swin Transformer network,this paper presents a scheme for the detection and recognition of defects in grid equipment.First,the original images are screened and extracted for high-frequency feature information using pseudo-color image enhancement mechanism and edge high-frequency information image segmentation mechanism.Then,a composite labeled feature pyramid module is introduced into the Swin Transformer backbone network to improve the success probability in extraction of multi-scale semantic features.Finally,a weakly supervised target selection mechanism is built for the detection and classification of targets in grid equipment images.Experimental results show that the algorithm proposed in this paper is effective in monitoring grid equipment and detecting its defects,the recognition accuracy reaching 92.6％.
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