Abstract
The bolts of the angle steel tower will rust, loose, and fall off under natural conditions. Traditional manual bolt defect detection is inefficient and dangerous. This paper proposes CViT-FRCNN based on ViT-FRCNN, which uses a convolutional neural network as the backbone model and the output features are input to the Transformer encoder. Compared with the direct patch embedding of ViT-FRCNN, this can improve the richness of input features and detection accuracy. A series of experiments show that our proposed model achieves the best performance in angle steel tower bolt defect detection and meets the needs of power inspection scenarios.
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