2
Related work
In this section, we introduce the related work that is most relevant to
our study. Firstly, a series of references about insulator detection are
reviewed in Section 2.1. Then, Section 2.2 contains scientific
literature on insulator segmentation, which is viewed as pixel-level
detection of the insulators.
2.1 Insulator detection
Object detection is to predict a
bounding box as an indication of the target’s category and location.
Similarly, insulator detection or insulator defect detection aims to
locate the insulators by surrounding them with bounding boxes and
identifying their categories or defect categories. Early methods about
insulator defect detection adopted a combination of computer vision and
machine learning technologies [1], [13], [14]. These methods
heavily relied on hand-crafted features, which were time-consuming to
design and required the assistance of experienced experts.
In recent years, deep learning-based detectors are introduced in the
application of insulator detection [17]–[20]. The studies can
be classified into one-stage and two-stage detectors. One-stage
detectors typically correlate to the You Only Look Once (YOLO) family of
deep neural networks [21]–[24], whereas two-stage detectors
include Region-based CNN (RCNN) and its variations [25]–[27].
Various one-stage detectors are used to identify the insulator’s
defective regions. Yang et al. incorporated a lightweight backbone into
the vanilla architecture of YOLO v3 to identify missing-cap insulators
[19]. The lightweight backbone is based on MobileNet [28] with
spatial pyramid pooling [37]. Similarly, a lightweight YOLO v4 is
also proposed in [20] to balance detection accuracy and detection
speed for insulator detection. Their lightweight techniques are
analogous, with MobileNet replacing the original backbone. Furthermore,
Han et al. presented TinyYOLO v4 that merged the self-attention module
into the Feature Pyramid Network (FPN) [38] to enhance channel-level
feature fusion [10]. This channel-wise self-attention facilitates
learning better feature representation. With the release of YOLO v5, its
pipeline was introduced into insulator detection research. In [39],
four versions of YOLO v5 were explored for the localization of the
insulator defect. As a result, the more suitable network architecture
was chosen through contrast experiments. Gao et al. modified the YOLO v5
pipeline by incorporating a triplet attention module in order to enhance
the detection performance of small insulator defects [9]. Then,
another attempt to incorporate attention mechanisms with the YOLO v5 was
reported in [29]. Lan et al. introduced the Convolutional Block
Attention Module (CBAM) to provide more channel and spatial context
information for insulator defect detection.
The methods listed above rely on one-stage detectors. Furthermore,
two-stage object detection frameworks were introduced into the insulator
detection community. In [17], [30], Faster RCNN was used to
first roughly localize the regions where insulators are most likely to
exist, referred to as ”proposals” in the framework. Then, these
proposals are fed into the second stage network, a multitask head, to
refine the localization of the insulators’ defects. Moreover, Tao et al.
model insulator defect detection as a two-level task that includes
insulator localization as well as defect detection [18]. The
framework is made up of two concatenated Faster RCNNs: one with a VGG16
backbone for localization and another with the original Faster RCNN for
detecting defective regions. Zhong et al. modified the standard Faster
RCNN pipeline to consider arbitrarily oriented insulator localization
[31]. The proposed framework introduced an oriented Region Proposal
Network (RPN) to implement arbitrarily oriented localization for
insulators. In [32], the attention mechanism was introduced in
Faster RCNN for self-explosion insulator defects. In detail, an adaptive
receptive field network is proposed and inserted into the FPN backbone.
2.2 Insulator segmentation
In other research works, the focus of the studies was to segment the
insulators or defective regions from the background. In [40], a
framework with two cascaded networks were proposed by Li et al. to
detect the insulators globally and segment the local defect objects. The
segmentation model was designed to incorporate an attention mechanism in
an improved version of U-Net [41]. Efficient Channel Attention
Networks (ECA-Net) was also introduced as the U-Net encoder, providing
an example of fusing an attention mechanism for insulator segmentation
[42]. Yu et al. focused on introducing fine-grained texture into the
SINet architecture and simultaneously improved a positioning network to
segment defective regions for insulators [2]. The insulator
segmentation problem was solved by Antwi-Bekoe et al. using a common
instance segmentation framework [43], in which the detection and
mask branches implemented instance-level segmentation. Xuan et al. used
a squeeze-excitation module to improve the backbone and a spatial
attention module to forecast the insulator mask to produce excellent
results in insulator defect segmentation [44].