Cross-Domain Multilevel Feature Adaptive Alignment R-CNN for Insulator Defect Detection in Transmission Lines

Abstract

Insulator defect detection is a crucial task in the intelligent inspection of transmission lines. Currently, there are challenges such as insufficient image samples and difficulties in annotation. Artificially generating samples is a feasible solution, but there are discrepancies between artificial and real sample distributions. This article proposes a cross-domain multilevel feature alignment R-CNN network. It uses a large number of artificially labeled images and a small number of unlabeled real images as the source and target domains, respectively. Based on Faster R-CNN, an instance-level feature adaptive alignment module is constructed. Different gradient adaptive training strategies are employed for the source and target domains to better achieve cross-domain instance-level feature alignment. An image-level multiscale local feature aggregation (MLA) module is built to achieve cross-domain image-level local feature alignment. A global feature alignment (GFA) module is also constructed to achieve cross-domain image-level global feature alignment. In the insulator defect detection experiment, the average precision (AP) at the Intersection over Union of 0.5 (AP50) of the proposed method is 6.8% higher than that of the baseline model, 8.6% higher than that of Faster-RCNN trained with artificial samples alone, and 1.1% higher than that of Faster-RCNN trained with real samples alone. Moreover, the proposed method achieves the highest accuracy on both self-built and public datasets, exhibiting an average improvement of 7.7% and 8.6% in AP50, respectively, compared to multiple comparable methods. The code for this article can be found at https://github.com/n-tong/CMFAA-R-CNN.