Detail R-CNN: Insulator Detection Based on Detail Feature Enhancement and Metric Learning
Feng Shuang, Shidi Wei, Yong Li, Xia Gu, Zhouxian Lu
Abstract
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.