Abstract

The elbow is an important constituent of oil and gas pipeline systems and plays a key role in changing the direction of pipelines. Corrosion defects pose a significant risk to the safe operation of elbows. Magnetic flux leakage (MFL) detection has been developed as a suitable technique for identifying defects in pipelines. To address the distortion of elbow defect signals in the images arising from variations in the liftoff value of the leakage detector, this paper proposed an image identification method based on an improved YOLOv5 network. The differences in defect images are simulated by analyzing the liftoff value of the magnetization unit. A defect image enhancement method of multiscale retinex with color restoration fusion homomorphic filtering (MSRCR-HF) is employed to enhance the features of defective MFL signal images. To further improve the accuracy of the model, the YOLOv5 network is optimized by integrating the convolutional block attention module (CBAM) and the space-to-depth-nonstrided convolution (SPD-Conv) module. The results show that the proposed image enhancement method effectively accentuates the features of defect images. Moreover, the suggested image identification method exhibits superior accuracy in identification. The mean average precision (mAP) values for the original image set and the enhanced image set are 85.0% and 91.4%, respectively. Consequently, the proposed method is shown to be highly viable for the automatic identification of MFL defects in small-diameter pipe elbows.

References

1.
Khalajestani
,
M. K.
,
Bahaari
,
M. R.
,
Salehi
,
A.
, and
Shahbazi
,
S.
,
2015
, “
Predicting the Limit Pressure Capacity of Pipe Elbows Containing Single Defects
,”
Appl. Ocean Res.
,
53
, pp.
15
22
.10.1016/j.apor.2015.07.002
2.
Lei
,
Y.
, and
Budden
,
P.
,
2022
, “
J Predictions for Defective Pipe Elbows Via the Reference Stress Method
,”
ASME J. Pressure Vessel Technol.
,
144
(
3
), p.
031303
.10.1115/1.4053286
3.
Piao
,
G.
,
Guo
,
J.
,
Hu
,
T.
,
Leung
,
H.
, and
Deng
,
Y.
,
2019
, “
Fast Reconstruction of 3-D Defect Profile From MFL Signals Using Key Physics-Based Parameters and SVM
,”
NDT E Int.
,
103
, pp.
26
38
.10.1016/j.ndteint.2019.01.004
4.
Azizzadeh
,
T.
, and
Safizadeh
,
M. S.
,
2019
, “
Estimation of the Diameters, Depths and Separation Distances of the Closely-Spaced Pitting Defects Using Combination of Three Axial MFL Components
,”
Measurement
,
138
, pp.
341
349
.10.1016/j.measurement.2019.02.077
5.
Zhang
,
M.
,
Guo
,
Y. B.
,
Zhang
,
Z.
,
He
,
R. B.
,
Wang
,
D. G.
,
Chen
,
J. Z.
, and
Yin
,
T.
,
2022
, “
Extraction of Pipeline Defect Feature Based on Variational Mode and Optimal Singular Value Decomposition
,”
Petrol Sci.
,
20
(
2
), pp.
1200
1216
.https://www.cup.edu.cn/petroleumscience/docs//2023-05/10ae826203ba47f28cfd1a3f9d4063e2.pdf
6.
Miao
,
X.
,
Zhao
,
H.
, and
Xiang
,
Z.
,
2023
, “
Leakage Detection in Natural Gas Pipeline Based on Unsupervised Learning and Stress Perception
,”
Process Saf. Environ. Prot.
,
170
, pp.
76
88
.10.1016/j.psep.2022.12.001
7.
Afzal
,
M.
, and
Udpa
,
S.
,
2002
, “
Advanced Signal Processing of Magnetic Flux Leakage Data Obtained From Seamless Gas Pipeline
,”
NDTE Int.
,
35
(
7
), pp.
449
457
.10.1016/S0963-8695(02)00024-5
8.
Sun
,
Y.
,
Liu
,
S.
,
Ye
,
Z.
,
Chen
,
S.
, and
Zhou
,
Q.
,
2016
, “
A Defect Evaluation Methodology Based on Multiple Magnetic Flux Leakage (MFL) Testing Signal Eigenvalues
,”
Res. Nondestruct. Eval.
,
27
(
1
), pp.
1
25
.10.1080/09349847.2015.1039100
9.
Wu
,
D.
,
Liu
,
Z.
,
Wang
,
X.
, and
Su
,
L.
,
2017
, “
Composite Magnetic Flux Leakage Detection Method for Pipelines Using Alternating Magnetic Field Excitation
,”
NDTE Int.
,
91
, pp.
148
155
.10.1016/j.ndteint.2017.07.002
10.
Liu
,
B.
,
He
,
L. Y.
,
Zhang
,
H.
,
Cao
,
Y.
, and
Fernandes
,
H.
,
2017
, “
The Axial Crack Testing Model for Long Distance Oil-Gas Pipeline Based on Magnetic Flux Leakage Internal Inspection Method
,”
Measurement
,
103
, pp.
275
282
.10.1016/j.measurement.2017.02.051
11.
Alzu'bi
,
A.
,
Amira
,
A.
, and
Ramzan
,
N.
,
2017
, “
Content-Based Image Retrieval With Compact Deep Convolutional Features
,”
Neurocomputing
,
249
, pp.
95
105
.10.1016/j.neucom.2017.03.072
12.
Ege
,
Y.
, and
Coramik
,
M.
,
2018
, “
A New Measurement System Using Magnetic Flux Leakage Method in Pipeline Inspection
,”
Measurement
,
123
, pp.
163
174
.10.1016/j.measurement.2018.03.064
13.
Banerjee
,
S.
, and
Das
,
S.
,
2018
, “
Mutual Variation of Information on transfer-CNN for Face Recognition With Degraded Probe Samples
,”
Neurocomputing
,
310
, pp.
299
315
.10.1016/j.neucom.2018.05.038
14.
Tzelepi
,
M.
, and
Tefas
,
A.
,
2018
, “
Deep Convolutional Learning for Content Based Image Retrieval
,”
Neurocomputing
,
275
, pp.
2467
2478
.10.1016/j.neucom.2017.11.022
15.
Zhang
,
M.
,
Guo
,
Y.
,
Xie
,
Q.
,
Zhang
,
Y.
,
Wang
,
D.
, and
Chen
,
J.
,
2022
, “
Defect Identification for Oil and Gas Pipeline Safety Based on Autonomous Deep Learning Network
,”
Comput. Commun.
,
195
, pp.
14
26
.10.1016/j.comcom.2022.08.001
16.
Li
,
F.
,
Feng
,
J.
,
Zhang
,
H.
,
Liu
,
J.
,
Lu
,
S.
, and
Ma
,
D.
,
2018
, “
Quick Reconstruction of Arbitrary Pipeline Defect Profiles From MFL Measurements Employing Modified Harmony Search Algorithm
,”
IEEE Trans. Instrum. Meas.
,
67
(
9
), pp.
2200
2213
.10.1109/TIM.2018.2813839
17.
Kandroodi
,
M. R.
,
Araabi
,
B. N.
,
Bassiri
,
M. M.
, and
Ahmadabadi
,
M. N.
,
2016
, “
Estimation of Depth and Length of Defects From Magnetic Flux Leakage Measurements: Verification With Simulations, Experiments, and Pigging Data
,”
IEEE Trans. Magn.
,
53
(
3
), pp.
1
1
.10.1109/TMAG.2016.2631525
18.
Badrinarayanan
,
V.
,
Kendall
,
A.
, and
Cipolla
,
R.
,
2017
, “
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
39
(
12
), pp.
2481
2495
.10.1109/TPAMI.2016.2644615
19.
Feng
,
J.
,
Li
,
F.
,
Lu
,
S.
,
Liu
,
J.
, and
Ma
,
D.
,
2017
, “
Injurious or Noninjurious Defect Identification From MFL Images in Pipeline Inspection Using Convolutional Neural Network
,”
IEEE Trans. Instrum. Meas.
,
66
(
7
), pp.
1883
1892
.10.1109/TIM.2017.2673024
20.
Bayar
,
B.
, and
Stamm
,
M. C.
,
2018
, “
Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection
,”
IEEE Trans. Inf. Forensics Secur.
,
13
(
11
), pp.
2691
2706
.10.1109/TIFS.2018.2825953
21.
Lu
,
S.
,
Feng
,
J.
,
Zhang
,
H.
,
Liu
,
J.
, and
Wu
,
Z.
,
2019
, “
An Estimation Method of Defect Size From MFL Image Using Visual Transformation Convolutional Neural Network
,”
IEEE Trans. Ind. Inf.
,
15
(
1
), pp.
213
224
.10.1109/TII.2018.2828811
22.
Zhang
,
Z. J.
,
Li
,
B. A.
,
Lv
,
X. Q.
, and
Liu
,
K. H.
,
2018
, “
Research on Pipeline Defect Detection Based on Optimized Faster R-CNN Algorithm
,”
DEStech Trans. Comput. Sci. Eng.
, pp.
469
474
.10.12783/dtcse/ammms2018/27322
23.
Yang
,
L. J.
,
Wang
,
Z. J.
,
Gao
,
S. W.
,
Shi
,
M.
, and
Liu
,
B. L.
,
2019
, “
Magnetic Flux Leakage Image Classification Method for Pipeline Weld Based on Optimized Convolution Kernel
,”
Neurocomputing
,
365
, pp.
229
238
.10.1016/j.neucom.2019.07.083
24.
Liu
,
S.
,
Sun
,
Y.
,
He
,
L.
,
Jiang
,
X.
, and
Kang
,
Y.
,
2019
, “
Quantitative MFL Characterization Study in Hard Magnetic Material Surface Topography Measurement
,”
Measurement
,
135
, pp.
503
519
.10.1016/j.measurement.2018.12.025
25.
Giorla
,
D.
,
Roccella
,
R.
,
Lo Frano
,
R.
, and
Sannazzaro
,
G.
,
2018
, “
EM Zooming Procedure in ANSYS Maxwell 3D
,”
Fusion Eng. Des.
,
132
, pp.
67
72
.10.1016/j.fusengdes.2018.04.096
26.
Lanari
,
P.
,
Vidal
,
O.
,
De Andrade
,
V.
,
Dubacq
,
B.
,
Lewin
,
E.
,
Grosch
,
E. G.
, and
Schwartz
,
S.
,
2014
, “
XMapTools: A MATLAB©-Based Program for Electron Microprobe X-Ray Image Processing and Geothermobarometry
,”
Comput. Geosci.
,
62
, pp.
227
240
.10.1016/j.cageo.2013.08.010
27.
Girija
,
M. G.
,
Shanavaz
,
K. T.
, and
Ajith
,
G. S.
,
2020
, “
Image Dehazing Using MSRCR Algorithm and Morphology Based Algorithm: A Concise Review
,”
Mater. Today: Proc.
,
24
, pp.
1890
1897
.10.1016/j.matpr.2020.03.614
28.
Wu
,
Y. F.
,
Li
,
M.
,
Zhang
,
Q. F.
, and
Liu
,
Y.
,
2018
, “
A Retinex Modulated Piecewise Constant Variational Model for Image Segmentation and Bias Correction
,”
Appl. Math. Model.
,
54
, pp.
697
709
.10.1016/j.apm.2017.10.018
29.
Mahmood
,
Z.
,
Muhammad
,
N.
,
Bibi
,
N.
,
Malik
,
Y. M
amd., and
Ahmed
,
N.
,
2018
, “
Human Visual Enhancement Using Multi Scale Retinex
,”
Inform. Med. Unlocked
,
13
, pp.
9
20
.10.1016/j.imu.2018.09.001
30.
Tang
,
Z.
,
Jiang
,
L.
, and
Luo
,
Z.
,
2021
, “
A New Underwater Image Enhancement Algorithm Based on Adaptive Feedback and Retinex Algorithm
,”
Multimedia Tools Appl.
,
80
, pp.
1
13
.10.1007/s11042-021-11095-5
31.
Orcioni
,
S.
,
Paffi
,
A.
,
Camera
,
F.
,
Apollonio
,
F.
, and
Liberti
,
M.
,
2018
, “
Automatic Decoding of Input Sinusoidal Signal in a Neuron Model: High Pass Homomorphic Filtering
,”
Neurocomputing
,
292
, pp.
165
173
.10.1016/j.neucom.2018.03.007
32.
Xu
,
Y.
,
Yang
,
C.
,
Sun
,
B.
,
Yan
,
X.
, and
Chen
,
M.
,
2021
, “
A Novel Multi-Scale Fusion Framework for Detail-Preserving Low-Light Image Enhancement
,”
Inf. Sci.
,
548
, pp.
378
397
.10.1016/j.ins.2020.09.066
33.
Mahaur
,
B.
, and
Mishra
,
K. K.
,
2023
, “
Small-Object Detection Based on YOLOv5 in Autonomous Driving Systems
,”
Pattern Recognit. Lett.
,
168
, pp.
115
122
.10.1016/j.patrec.2023.03.009
34.
He
,
K.
,
Zhang
,
X.
,
Ren
,
S.
, and
Sun
,
J.
,
2015
, “
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
37
(
9
), pp.
1904
1916
.10.1109/TPAMI.2015.2389824
35.
Jubayer
,
F.
,
Soeb
,
J. A.
,
Mojumder
,
A. N.
,
Paul
,
M. K.
,
Barua
,
P.
,
Kayshar
,
S.
,
Akter
,
S. S.
,
Rahman
,
M.
, and
Islam
,
A.
,
2021
, “
Detection of Mold on the Food Surface Using YOLOv5
,”
Curr. Res. Food Sci.
,
4
, pp.
724
728
.10.1016/j.crfs.2021.10.003
36.
Chowdhury
,
P. N.
,
Shivakumara
,
P.
,
Nandanwar
,
L.
,
Samiron
,
F.
,
Pal
,
U.
, and
Lu
,
T.
,
2022
, “
Oil Palm Tree Counting in Drone Images
,”
Pattern Recognit. Lett.
,
153
, pp.
1
9
.10.1016/j.patrec.2021.11.016
37.
Chen
,
H.
,
Qi
,
Y.
,
Yin
,
Y.
,
Li
,
T.
,
Liu
,
X.
,
Li
,
X.
,
Gong
,
G.
, and
Wang
,
L.
,
2020
, “
MMFNet: A Multi-Modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma
,”
Neurocomputing
,
394
, pp.
27
40
.10.1016/j.neucom.2020.02.002
38.
Sunkara
,
R.
, and
Luo
,
T.
,
2022
, “
No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects
,” arXiv: 2208.03641.
39.
Peng
,
H.
,
Huang
,
B.
,
Shao
,
Y.
,
Li
,
Z.
,
Zhang
,
C.
,
Chen
,
Y.
, and
Xiong
,
J.
,
2018
, “
General Improved SSD Model for Picking Object Recognition of Multiple Fruits in Natural Environment
,”
Trans. Chin. Soc. Agric. Eng.
,
34
, pp.
155
162
.10.11975/j.issn.1002-6819.2018.16.020
40.
Jiang
,
D.
,
Li
,
G.
,
Tan
,
C.
,
Huang
,
L.
,
Sun
,
Y.
, and
Kong
,
J.
,
2021
, “
Semantic Segmentation for Multiscale Target Based on Object Recognition Using the Improved Faster-RCNN Model
,”
Future Gener. Comput. Syst.
,
123
, pp.
94
104
.10.1016/j.future.2021.04.019
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