Insulators need to be regularly inspected to ensure the normal operation of the power system. Currently, the detection of insulators in power distribution network poses greater challenges due to the wider variety of types and smaller differences between classes. Furthermore, most of distribution network insulators are small targets, background will generate significant interference. To enhance detail in feature maps, a detail feature enhancement module is proposed. In the proposed module, shallow features are integrated into deep features, and object regions of the feature maps are adjusted by attention mechanism. Additionally, to enhance the discriminant ability of classification features, an auxiliary classification module is introduced. Typical insulators are used as examples and category score of the detected insulators are adjusted, thereby improving the ability to distinguish similar insulators. Finally, the proposed auxiliary classification module is employed to construct an insulator detection algorithm based on Faster R-CNN. Notably, the experimental results conducted on a distribution network insulator dataset show that the proposed algorithm can achieve a mean average precision of 96.6%, which can accurately identify the majority of insulators.

Due to inefficiency and insecurity of manual insulator inspection, research on intelligent insulator inspection has received widespread attention. Previous deep learning-based defect detection methods rely excessively on big data training. However, in reality, defective insulator samples are too scarce to meet the training requirements of traditional neural networks. To overcome this problem, this paper proposes a few-shot learning based two-stage insulator defect detection algorithm (InsDef).InsDef consists of two stages: the insulator extraction stage and the defect recognition stage. The insulator extraction stage uses YOLOv5 to locate and extract insulators in aerial image.Then, in the defect recognition stage, we use the proposed few-shot learning based defect recognition network (FSDef) to diagnose the health of insulators.FSDef employs a dual-guide attention mechanism (DGA) and multiple label consistency constraints (MLC), which enhances the network's ability to detect unobvious defects and improve network's generalization. The experimental results show that InsDef achieved a precision of 77.83%, outperforming other advanced methods. Additionally, the model size of 22.62mb and a run time of 47.01ms demonstrate the practical application value of InsDef.

Due to the low efficiency and safety of a manual insulator inspection, research on intelligent insulator inspections has gained wide attention. However, most existing defect recognition methods extract abstract features of the entire image directly by convolutional neural networks (CNNs), which lack multi-granularity feature information, rendering the network insensitive to small defects. To address this problem, we propose a multi-granularity fusion network (MGFNet) to diagnose the health status of the insulator. An MGFNet includes a traversal clipping module (TC), progressive multi-granularity learning strategy (PMGL), and region relationship attention module (RRA). A TC effectively resolves the issue of distortion in insulator images and can provide a more detailed diagnosis for the local areas of insulators. A PMGL acquires the multi-granularity features of insulators and combines them to produce more resilient features. An RRA utilizes non-local interactions to better learn the difference between normal features and defect features. To eliminate the interference of the UAV images’ background, an MGFNet can be flexibly combined with object detection algorithms to form a two-stage object detection algorithm, which can accurately identify insulator defects in UAV images. The experimental results show that an MGFNet achieves 91.27% accuracy, outperforming other advanced methods. Furthermore, the successful deployment on a drone platform has enabled the real-time diagnosis of insulators, further confirming the practical applications value of an MGFNet.