Citation: Wang, C.; Sun, M.; Cao, Y.;
He, K.; Zhang, B.; Cao, Z.; Wang, M.
Lightweight Network-Based Surface
Defect Detection Method for Steel
Plates. Sustainability 2023, 15, 3733.
https://doi.org/10.3390/su15043733
Academic Editors: Luis Hernández-
Callejo, Sergio Nesmachnow and
Sara Gallardo Saavedra
Received: 18 November 2022
Revised: 9 February 2023
Accepted: 9 February 2023
Published: 17 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
Lightweight Network-Based Surface Defect Detection Method
for Steel Plates
Changqing Wang
1,2,3
, Maoxuan Sun
1,2,3
, Yuan Cao
1,2,3,
*, Kunyu He
1,2,3
, Bei Zhang
1,2,3
, Zhonghao Cao
1,2,3
and Meng Wang
1,2,3
1
College of Electronics and Electrical Engineering, Henan Normal University, Xinxiang 453007, China
2
Henan Key Laboratory of Optoelectronic Sensing Integrated Application, Xinxiang 453007, China
3
Henan Engineering Laboratory of Additive Intelligent Manufacturing, Xinxiang 453007, China
* Correspondence: xyuan_cao@163.com
Abstract:
This article proposes a lightweight YOLO-ACG detection algorithm that balances accuracy
and speed, which improves on the classification errors and missed detections present in existing
steel plate defect detection algorithms. To highlight the key elements of the desired area of surface
flaws in steel plates, a void space convolutional pyramid pooling model is applied to the backbone
network. This model improves the fusion of high- and low-level semantic information by designing
feature pyramid networks with embedded spatial attention. According to the experimental findings,
the suggested detection algorithm enhances the mapped value by about 4% once compared to the
YOLOv4-Ghost detection algorithm on the homemade data set. Additionally, the real-time detection
speed reaches about 103FPS, which is about 7FPS faster than the YOLOv4-Ghost detection algorithm,
and the detection capability of steel surface defects is significantly enhanced to meet the needs of
real-time detection of realistic scenes in the mobile terminal.
Keywords: defect detection; lightweight; cavity spatial convolution; spatial attention
1. Introduction
With the rapid development of industrial automation technology, the study of au-
tomated [
1
,
2
] detection of defects in industrial production is receiving more and more
attention. Due to the influence of various uncertainties, the surface of the steel plate in the
production process will produce a variety of defects [3–7], such as scratches, deformation,
welds, holes, etc. These defects [
8
–
12
] not only affect the integrity of the steel plate but
also make a certain impact on the quality of the steel plate, so a more accurate detection of
defects [13–16] on the surface of the steel plate is of paramount importance.
Conventional inspection methods use manual observation to detect defects, which
is not only time-consuming and labor-intensive, but the results still do not meet the ex-
pected requirements. Based on the traditional industrial inspection methods proposed,
the automated defect detection technology has been driven to a new level. Experts and
scholars at home and abroad have conducted more profound research and practice on
traditional machine vision in the detection of defects in steel plates. The enhanced BP
detection algorithm was presented by Peng et al. [
17
] to detect flaws in steel plates. While
this technique has a decent detection performance for flaws that are clear targets, it has a
sluggish convergence rate and poor performance for small samples. Wang Yixin et al. [
18
]
suggested a comparative detection approach utilizing machine vision; however, despite its
high accuracy in recognizing faults in steel plates, it has a higher environmental impact
and is incapable of detecting flaws in harsh conditions due to its difficulty with extracting
feature images.
At this juncture, the accuracy of steel plate surface flaw detection [
19
] has increased due to
the rapid development of deep learning technology in industrial inspection.
Tian Siyang et al.
investigated at timeframe instances of hot-rolled strip steel surface faults, identifying two faults,
Sustainability 2023, 15, 3733. https://doi.org/10.3390/su15043733 https://www.mdpi.com/journal/sustainability
