Type: xxx
Electrical insulator defect detection with incomplete annotations and
imbalanced samples
Fengqian Pang1, *, Chunyue
Lei1, and Jingsheng
Zeng1
1School of Information Science and Technology, North
China University of Technology, Beijing, China
*Corresponding Author: fqpang@ncut.edu.cn
Received: XX Month 202X; Accepted: XX Month 202X
Abstract: Insulators are one of the key components in
high-voltage power systems that prevent transmission lines from
grounding. Since they are exposed to different kinds of harsh
environments and climates, periodic inspection is indispensable for the
safety and high quality of power grid. Nowadays, Unmanned Aerial Vehicle
(UAV) inspection is more widely used, facilitating incorporation of
CNN-based detectors in the insulator detection task. However, these
methods are generally based on the assumption that the image samples are
balanced among different categories and possess completely ideal
annotations. The problem of sample imbalance or incomplete annotation is
rarely investigated in depth for insulator defect detection. In this
paper, we focus on insulator defect detection with imbalanced data and
incomplete annotations. Our proposed framework, named Pi-Index,
introduces Positive Unlabeled (PU) learning to solve the problem of
incomplete annotation and designs a novel index the class prior, which
is a key parameter in PU learning. Moreover, focal loss is integrated in
our framework to alleviate the effect of sample imbalance. Experiment
results demonstrate that the proposed framework achieves better
performance than the baseline methods in situations of sample imbalance
and missing annotation.Keywords: Insulator defect detection; Transmission line; Power
system; Sample imbalance; Incomplete annotation