Pi-Score: An Estimation Strategy of the Class Prior in Positive-Unlabeled Learning for Electrical Insulator Defect Detection with Incomplete Annotations

Abstract

Insulators in high-voltage power systems serve as brackets for overhead lines and prevent these wires from becoming grounded. Due to long-term exposure to a harsh environment, it is indispensable to apply periodic inspection for defective insulators, facilitating the timely overhaul of insulators. In the field of object detection, convolutional neural networks (CNNs) have been introduced and have achieved good performance. Therefore, various CNN-based detectors are applied to the insulator detection task. Because collecting ideal annotations is time-consuming and labor-intensive, research with imperfect annotations has drawn more attention. However, fewer insulator defect detection approaches account for this imperfect annotation problem. This paper focuses on a novel and challenging insulator defect detection scenario: a part of the insulators in datasets is unannotated and viewed as the background. We introduce positive-unlabeled (PU) learning to solve the problem of incomplete annotation for insulator defect detection. To further improve PU learning, a Pi-Score algorithm is proposed to estimate class prior, a crucial parameter in PU loss. Our designed framework is built on Faster R-CNN and incorporates the improved PU learning in the region proposal network. Experimental results on the Insulator Defect Image Dataset (IDID) demonstrate that the proposed framework achieved an average precision (AP) metric that is approximately 1%–2% higher than the positive–negative mainstream detectors with varying degrees of missing annotation. Meanwhile, the proposed framework obtained 0.33 and 0.47 higher AP metrics than the mainstream PU detectors with complete and half of IDID’s annotations.