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Journals >
Laser & Optoelectronics Progress >
Volume 58 >
Issue 12 >
Page 1210022 > Article
Laser & Optoelectronics Progress
Vol. 58, Issue 12, 1210022 (2021)
Insulator Defect Recognition in Aerial Images Based on Gaussian YOLOv3
Quan Wang
1
and Benshun Yi
2、*
Author Affiliations
1
FiberHome Technologies Group, Wuhan, Hubei 430074, China
2
Wuhan University, Wuhan, Hubei 430072, China
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DOI:
10.3788/LOP202158.1210022
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Quan Wang, Benshun Yi. Insulator Defect Recognition in Aerial Images Based on Gaussian YOLOv3[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210022
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Fig. 1.
Image of the insulator. (a) Normal image; (b) image with defects
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Fig. 2.
Structure of the YOLOv3
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Fig. 3.
Test results of the YOLOv3. (a) Target is not completely wrapped; (b) duplicate frame
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Fig. 4.
Output of the YOLOv3
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Fig. 5.
Output of the Gaussian YOLOv3
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Fig. 6.
Training losses in different situations. (a) Step 1); (b) step 2); (c) step 3)
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Fig. 7.
Detection results of the Gaussian YOLOv3. (a) Normal insulator; (b) insulator with defects
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Data set
Insulator
Defect
Training set
1727
713
Validation set
193
79
Test set
480
200
Table 1.
Division of the data set
Algorithm
Insulator /%
Defect /%
mAP /%
X
FPS
/(frame·s
-1
)
P
i
/%
P
d
/%
Faseter R-CNN
91.5
83.0
87.1
5.4
32.5
22.0
YOLOv3
93.5
85.0
90.3
26.3
29.6
24.0
Guassian YOLOv3
93.8
94.5
94.3
26.1
91.7
94.0
Table 2.
Test results of different algorithms
Abstract
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Quan Wang, Benshun Yi. Insulator Defect Recognition in Aerial Images Based on Gaussian YOLOv3[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1210022
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Paper Information
Category: Image Processing
Received: Aug. 24, 2020
Accepted: Nov. 4, 2020
Published Online: Jun. 17, 2021
The Author Email: Yi Benshun (yibs@whu.edu.cn)
DOI:
10.3788/LOP202158.1210022
Recommended Topics
laser devices and laser physics
Lasers and Laser Optics
Laser physics
laser manufacturing
Instrumentation, Measurement and Metrology
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