As the largest number of fasteners on power distribution network, bolts are the cornerstone of ensuring the safety and reliability of power system. However, pin losting, nut losting, nut loosening, and rusting can cause damage to power system and even cause terrible accidents. In order to solve the problem that the large number of bolt defects causes traditional manual identification to be difficult and inefficient, this paper proposes a bolt defect identification algorithm based on attention models. The method in this paper improves the traditional deep residual network ResNet network, adds a channel attention mechanism to obtain key channel features, and uses random flipping, translation and other data augment methods to expand the bolt defect dataset. The experimental results show that compared with the traditional model, the improved model can more accurately identify different types of bolt defect images, and the mAP on the testing set reaches 85.9%, which verifies the feasibility and reliability of the ATT-ResNet50 model in bolt defect recognition. The method proposed in this paper has high recognition accuracy, realizes the intelligent recognition of common bolt defects.
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