[1]QI Yincheng,GENG Shaofeng,ZHAO Zhenbing,et al.A method for super resolution processing of bolt image based on feature transfer[J].CAAI Transactions on Intelligent Systems,2023,18(4):858-866.[doi:10.11992/tis.202201009]
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A method for super resolution processing of bolt image based on feature transfer

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 4 Page number: 858-866 Column: 人工智能院长论坛 Public date: 2023-07-15
Title:
A method for super resolution processing of bolt image based on feature transfer
Author(s):
QI Yincheng1; 2;  GENG Shaofeng1;  ZHAO Zhenbing1; 2;  LYU Xuechun1;  SUN Meng1
1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China
Keywords:
bolt; image super resolution; deep learning; feature transfer; transmission line; defect recognition; neural network; ntelligent patrol
CLC:
TP391; TM726
DOI:
10.11992/tis.202201009
Abstract:
Aiming at the problems of blurring and low resolution of bolt images collected during inspection of transmission lines, a super-resolution processing method of bolt images based on feature migration is proposed according to the characteristics of high similarity between bolts. In this paper, feature transfer is introduced into bolt image super resolution for the first time. First, compare feature regions of the low-resolution image and the clear reference image, then carry out feature transfer of the regions with high similarity between the images, and then adjust the proportion of transfer features according to similarity of the transferred features. Then, the constraint of similarity of the transfer features is added to the perceptual loss function to ensure accuracy of the transfer features. The results of the bolt image over-segmentation experiments with different over-segmentation models show that the bolt over-segmentation images of this method are clearer, and the PSNR and SSIM indicators are better; The defect recognition accuracy rate of the bolt increased by 3.61% after ove-segmentation. The experimental results fully verify effectiveness of this method in solving the problem of blurred bolt images.
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