1 Introduction

Transmission of electricity throughout the power grid is accomplished with the assistance of high-voltage transmission lines, electrical insulators, and power towers. Although the insulators therein are not directly responsible for delivering electricity, they suspend the overhead transmission lines and prevent the transmission lines’ grounding [1]–[5]. Furthermore, they must constantly withstand the supply voltage and bear the load of the transmission lines’ gravity. Therefore, the insulator is an essential component in a high-voltage power system, which facilitates maintaining the safety and stabilization of the power grid.
Because of the extensive deployment of the power grid, the insulators need to be adaptable to different kinds of natural environments and geographic conditions. A fairly large number of them are exposed to an outdoor environment throughout the entire year, which is more susceptible to the erosion caused by harsh climates such as sunlight, rain, and snow [6]. Besides, overvoltage shocks from lightning and on-off operations, mechanical load, weight of wires, as well as metal accessories may also make the insulators more easily prone to self-blast or breakage [7]. The above factors inevitably give rise to the insulators’ defects and reduce their service lifetimes. Moreover, aged and defective insulators may cause regional grid failures and enormous economic losses if they are not periodically inspected [2].
One solution is manual inspection of power equipment, but this traditional method is considered low-efficiency, labor-intensive, and unsafe. It is incapable of providing quick feedback on the condition of insulators and meeting the inspection needs of a modern smart grid, namely more frequent inspection, repair, and maintenance [8]. Due to the low cost, miniaturization, and high mobility of Unmanned Aerial Vehicle (UAV) inspection, it has replaced manual inspection and become the mainstream inspection scheme for power equipment [9]–[12]. For insulator defect detection, the UAV inspection collects a large number of aerial images with insulators. The collected insulator images not only enable the automatic identification of the insulator defects, but also make it an urgent problem to be solved.
Early algorithms for insulator defect detection are based on handcrafted features and machine learning technologies [1], [13], [14]. However, feature design is time-consuming and costly, and requires the assistance of experienced experts. In recent years, deep learning technology has made a breakthrough in image classification [6], object detection [15], image segmentation [16], etc. The deep learning-based detectors were introduced in this field [17]–[20], including both You Only Look Once (YOLO) family [21]–[24] and Region-based CNN (RCNN) series [25]–[27] detectors. On the one hand, the YOLO-based detectors were investigated to enhance the speed of insulator detection by incorporating a lightweight backbone, such as, replacing the backbone by MobileNet [28] in YOLO v3 [23] and YOLO v4 [24], and choosing suitable backbone among the four versions of YOLO v5 [9]. Simultaneously, attention mechanisms have been introduced into insulator detection research. In detail, channel-wise self-attention was merged with TinyYOLO v4 to facilitate feature representation [10], while YOLO v5 pipeline was incorporated with a triplet attention module in [9] and a Convolutional Block Attention Module (CBAM) in [29] for providing more context information for insulator defect detection.
On the other hand, Faster RCNN, a two-stage detector, was introduced into the insulator detection community [17], [30]. It first roughly generates the proposals of insulators and then refines the proposals for locating the insulators’ defects. Moreover, Zhong et al. modified the standard Faster RCNN pipeline to consider arbitrarily oriented insulator localization [31]. In [32], the attention mechanism was introduced in Faster RCNN for self-explosion insulator defects. In summary, the aforementioned approaches focused on the following problems: 1) A complex background and various types of insulators make insulator defect detection difficult; 2) some severely damaged insulators are too small to recognize; 3) detection speed and accuracy needs to be balanced.
In most fields of object detection, ”perfect annotation” indicates that the labeled bounding boxes are close to the targets’ true boundaries and there are no missing labels. Therefore, the acquisition of the perfect annotation is time-consuming and labor-intensive. To decrease the dependency on perfect annotation, numerous research works have studied the different scenarios of imperfect annotation, such as incomplete annotation [33], [34], unreliable labels [35], and incremental new categories [36]. However, the incomplete annotation problem of insulator defect detection is not thoroughly investigated. Besides, there is a sample imbalance among different categories in our task.
In this paper, we propose a novel framework for incomplete annotation and sample imbalance in insulator defect detection. The proposed framework is based on the Faster RCNN detector and integrates Positive Unlabeled (PU) learning and focal loss. It is termed Pi-Index that follows the name of the proposed algorithm for estimating the class prior, a key parameter in PU loss, to improve PU learning. The algorithm is designed to generate a continuous value (Pi-Index) for each anchor as its probability of being positive. In the aspect of network architecture, Region Proposal Network (RPN) is improved by introducing PU learning to overcome the problem of incomplete annotation, whereas the focal loss strategy is applied to Region Of Interest (ROI) Head, to alleviate the impairment caused by sample imbalance.
The contributions of this paper are summarized in the following aspects: 1) A novel estimation strategy for the class prior, termed Pi-Index, is proposed to improve vanilla PU learning; 2) The PU learning strategy and focal loss are separately incorporated with RPN and ROI Head, which are responsible for the above two problems; and 3) Experiment results show that the proposed framework achieves better performance compared with baseline methods when missing labels or sample imbalance scenarios occur.
The rest of this paper is organized as follows. Section 2 describes the proposed framework that contains FPN backbone, RPN with PU learning and ROI Head with focal loss. Section 3 is about experimental results that prove the effectiveness of the proposed framework. Section 4 gives a summary of this paper.