A recognition algorithm for power failures based on super-pixel segmentation is proposed as a solution to the challenging problem of extracting mesh targets in power scenarios.Firstly,the super-pixel segmentation algorithm is applied in LAB space to implement the segmentation,and improved K-clustering is used to generate mesh clusters.Secondly,for the difficulty to classify the mesh clusters,a dual attention mechanism Mobile Net V2 network is proposed,and a target object mask is extracted by the fusion result of similar grid clusters after the classification.The training is conducted on dataset comprising transmission line towers and metal shielding mesh for converter valve inspection channels,and the edge strengthening experiment is done,obtaining a higher accuracy and conducting.
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