4.1
Dataset
4.1.1 Dataset description
The Insulator Defect Image Dataset
(IDID)11The Insulator Defect Image Dataset refers to:
https://ieee-dataport.org/competitions/insulator-defect-detection, a
widely-used insulator dataset, is utilized for model assessment. As
indicated in Figure 6, the IDID comprises several high-resolution aerial
images with insulators and the corresponding annotations. Each
annotation consists of a category label and a bounding box. Insulators
are classified as ”Good,” ”Broken,” and ”FlashDamaged.” Additionally,
there exists a fourth category termed as “Insulator String”, which
refers to a cluster of insulators depicted in the images. Figure 6
depicts the many categories of bounding boxes with a variety of colors.
The IDID training set comprises a total of 1600 aerial images,
encompassing 2636 “Good”, 1140 “Broken”, and 2004 “FlashDamaged”
insulator shells. These insulator shells collectively form 1788
insulator strings.
However, there exists a portion of images with incomplete annotations in
IDID. Figure 7 displays several aerial images in which some insulators
are incorrectly annotated. Specifically, the yellow boxes in the second
row of images represent unlabeled insulators.
The incomplete annotations could
potentially stem from the dense arrangement of insulators and the
oversight on part of the annotators. In many studies [], the IDID
dataset has been utilized as a perfectly annotated dataset despite the
presence of missing annotations. Therefore, regarding IDID as a
partially annotated dataset is more reasonable and this partially
annotated scenario studied in this paper has practical significance.
4.1.2 Dataset split
In order to eliminate interference from the sample imbalance, the model
evaluation is performed on validation and test sets with category
balance. The number of broken insulator shells is the lowest among the
four classes. The samples from this category are distributed into the
training, validation, and test sets in a ratio of 5:2:3. The validation
and test sets for each category contain 228 and 342 samples,
respectively. Therefore, we randomly select 228 and 342 samples from
each category to form the validation and test sets, respectively. The
remaining samples from each category are mixed together to form the
training set, which consists of 1218 insulator strings, 1756 good
insulator shells, 570 broken insulator shells, and 1434 flashover
damaged insulator shells. In all our experiments, we have set the random
seed to 1.
4.2 Experimental setup
4.2.1 Implementation
details
To verify our method with different
proportions of annotations, we
randomly remove a portion of the annotations from the training set.
The Annotation
PerCent (APC) represents the
percentage of annotations that remain after the aforementioned removal
procedure. The other significant hyper-parameters are delineated as
follows: The batch size is set to 16, the learning rate is set to 0.02,
the total number of iterations is 10,000, and the evaluation interval
for the validation set is 200 iterations. The best model is selected
according to the AP metric on the validation set, which is then
applied to the test set. Finally, the data augmentation in our framework
contains the horizontal and vertical flips as well as the default data
augmentation strategy in Detectron222The github repository of
Detectron2: https://github.com/facebookresearch/detectron2.
4.2.2 Software platform
This experiment was conducted on a
server with the Linux system and used Visual Studio Code (VSCode) as our
Integrated Development Environment (IDE). PyTorch and Python were
selected as the deep learning toolkit and the programming language,
respectively. The hardware of the server is mainly composed of two
Intel(R) Xeon(R) E5-2680 v4 CPUs
with 14 cores each running at 2.4 GHz, 256 GB memory, and two Nvidia
GeForce RTX 3090 GPUs.
4.2.3 Adopted baselines
To verify the effectiveness of our
proposed method on incomplete annotation data, we conducted experiments
to compare our method with other mainstream methods under different APCs
(1, 0.7, 0.5, and 0.3). Our proposed framework is a Positive-Unlabeled
(PU) framework, which is viewed as a combination of a Positive-Negative
(PN) pipeline and PU loss.
Therefore, we first selected existing mainstream Positive-Negative (PN)
learning object detection algorithms [31], [32] to ablate the
influence of PU loss. Furthermore, we also introduced several PU-based
detectors [34], [49] as contrast methods.
PN-based object detection algorithms
typically include two frameworks: one-stage and two-stage. The existing
one-stage frameworks for insulator detection are usually based on YOLO
v3 and YOLO v4 with MobileNet backbone, abbreviated as M-YOLO v3
[19] and M-YOLO v4 [20], respectively. Since our study does not
focus on lightweight computing, we combined DarkNet53 with the
aforementioned YOLO frameworks (D-YOLO v3 [23] and D-YOLO v4
[24]), as well as YOLO v5 (D-YOLO v5) [39]. Because
Table 2: Detection results of our method and other methods
based on the complete annotation supported by IDID.