Abstract: In order to solve the problem that the existing algorithm of substation defect image detection and recognition cant work effectively, a method of defect image detection and recognition of substation equipment based on attention mechanism learning is proposed. The proposed method uses convolutional neural network as the skeleton network of defect image feature extraction, and integrates the principle of attention mechanism to further improve the recognition ability of defect image features. Firstly, the convolution neural network feature extraction model of attention mechanism is constructed to extract the features of substation defect image under different attention mechanisms; secondly, an adaptive feature learning function is designed to fuse the features into new high-quality substation defect image features under different attention mechanisms; finally, the defect image features under different attention mechanisms are input into the classification model to realize the detection and recognition of substation defect image. The proposed method can improve the accuracy and robustness of defect detection. Extensive experiments show that the accuracy mAP of this method is 70.4%.

Key words: attention mechanism, substation equipment, defect image, convolutional neural network