Abstract: In order to solve the problem that the small number of insulator defect samples and small defect targets lead to the current low accuracy of insulator defect detection,this paper proposes a few-shot object detection model(C-TFSIDD) combining CNN and Transformer,which fuses local and global features of images to realize insulator defect detection more effectively.Firstly,Next-ViT,which integratesthe local detail capture capability of CNN and the global information integration capability of Transformer,is used as the feature extraction module to accurately capture local and global feature information of insulator images.Secondly,the improved path aggregation feature pyramid network(PAFPN) is used for bidirectional multi-scale feature fusion to enhance the underlying feature representation and improve the detection effect of small targets.Finally,a metric-based discriminative loss is proposed to optimize the classifier in the fine-tuning stage to learn more discriminative feature representations to increase the separability between classes and reduce the effect of intra-class variations.Trained and evaluated on two public insulator defect datasets,the experimental results show that C-TFSIDD improves the detection results with samples of 5shot,10shot,and 20shot by 28.7%,35.5%,and 47.7%,respectively,compared to the baseline model TFA,and compared with the few-shot object detection model FSCE,the proposed method improved by 21.8%,26.7%,and 21.1%,respectively.The results show that C-TFSIDD can effectively improve the defect detection accuracy of few-shot insulator samples.

Key words: Defect detection, Insulator, Few-shot, CNN-Transformer, Metric learning