Citation: Han, G.; Yuan, Q.; Zhao, F.;
Wang, R.; Zhao, L.; Li, S.; He, M.;
Yang, S.; Qin, L. An Improved
Algorithm for Insulator and Defect
Detection Based on YOLOv4.
Electronics 2023, 12, 933. https://
doi.org/10.3390/electronics12040933
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and
Manolis Maragoudakis
Received: 18 January 2023
Revised: 6 February 2023
Accepted: 10 February 2023
Published: 13 February 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
An Improved Algorithm for Insulator and Defect Detection
Based on YOLOv4
Gujing Han
1,2,
*, Qiwei Yuan
1,2
, Feng Zhao
3
, Ruijie Wang
1,2
, Liu Zhao
1,2
, Saidian Li
1,2
, Min He
4
, Shiqi Yang
4
and Liang Qin
4
1
School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
2
State Key Laboratory of New Textile Materials and Advanced Processing Technologies,
Wuhan Textile University, Wuhan 430200, China
3
State Grid Information & Telecommunication Group Co., Ltd., Beijing 102211, China
4
School of Electrical and Automation, Wuhan University, Wuhan 430072, China
* Correspondence: gjhan@wtu.edu.cn
Abstract:
To further improve the accuracy and speed of UAV inspection of transmission line insulator
defects, this paper proposes an insulator detection and defect identification algorithm based on
YOLOv4, which is called DSMH-YOLOv4. In the feature extraction network of the YOLOv4 model,
the improved algorithm improves the residual edges of the residual structure based on feature reuse
and designs the backbone network D-CSPDarknet53, which greatly reduces the number of parameters
and computation of the model. The SA-Net (Shuffle Attention Neural Networks) attention model is
embedded in the feature fusion network to strengthen the attention of target features and improve
the weight of the target. Multi-head output is added to the output layer to improve the ability of
the model to recognize the small target of insulator damage. The experimental results show that
the number of parameters of the improved algorithm model is only 25.98% of that of the original
model, and the mAP (mean Average Precision) of the insulator and defect is increased from 92.44% to
96.14%, which provides an effective way for the implementation of edge end algorithm deployment.
Keywords: UAV inspection; insulator defect; DSMH-YOLOv4; feature reuse; SA-Net; multi-head
1. Introduction
As an important part of the overhead transmission line, insulators assume the role
of mechanical support and electrical insulation of the line [
1
,
2
]. In addition, insulators
work under high voltage and high load for a long time and are often eroded by all kinds
of bad weather. Insulators are thus prone to damage, which seriously threatens the safety
and stability of transmission lines [
3
,
4
]. Therefore, the detection of insulators and defects
on high-voltage transmission lines has become an important task in power inspection. In
recent years, due to its rapid rise, drone inspection technology has begun to gradually
replace manual inspections [
5
,
6
]. To achieve real-time high-precision detection of insulators
and their defects by UAVs [
7
], the ability to deploy detection algorithms on UAVs at the
edge is a necessary prerequisite [
8
,
9
]. At the same time, it is necessary to further improve
the recognition speed and accuracy of the algorithm for small targets such as insulator
defects in complex backgrounds, to improve the algorithm performance and increase the
detection efficiency [10,11].
With the continuous development of deep learning technology [
12
], methods related to
the detection of insulators and their defects have been proposed one after
another [13–15]
. At
present, the commonly used deep learning algorithms are mainly divided into
two categories
:
the first category comprises the regression-based one-stage algorithms, such as SSD (Sin-
gle Shot MultiBox Detector), YOLO (You Only Look Once), YOLOv2, YOLOv3, and
YOLOv4 [
16
–
20
]. The second category comprises the two-stage algorithm based on re-
gion candidates, such as R-CNN (Region-Convolutional Neural Network), Fast R-CNN,
Electronics 2023, 12, 933. https://doi.org/10.3390/electronics12040933 https://www.mdpi.com/journal/electronics
