Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 319-324.doi: 10.11896/jsjkx.210500124

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Dam Crack Detection Based on Multi-source Transfer Learning

WANG Jun-feng1,2, LIU Fan1,2, YANG Sai3, LYU Tan-yue1,2, CHEN Zhi-yu1,2, XU Feng2   

  1. 1 Key Laboratory of Ministry of Education for Coastal Disaster and Protection,Hohai University,Nanjing 210098,China
    2 College of Computer Information,Hohai University,Nanjing 210098,China
    3 School of Electrical Engineering,Nantong University,Nantong,Jiangsu 226019,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:WANG Jun-feng,born in 1996,postgraduate,is a member of China Computer Federation.His main research interests include object detection and semantic segmentation.
    LIU Fan,born in 1988,Ph.D,professor,is a member of China Computer Federation.His main research interests include pattern recognition and computer vision.
  • Supported by:
    Natural Science Foundation of Jiangsu Province(BK20191298),Development Fund of Key Laboratory of Coastal Disaster and Protection of Ministry of Euducation,Hohai University(20150009) and Fundamental Research Funds for the Central Universities(B200202175).

Abstract: The existing deep models will encounter overfitting and low computational efficiency when they are directly used for dam crack detection.This paper proposes a new dam crack detection algorithm based on multi-source transfer learning,which aims to improve the accuracy,reduce the model calculation and speed up the detection speed.Firstly,this method combines MobileNet with SSD object detection algorithm to construct a MobileNet-SSD network,which effectively reduces model parameters and computational complexity.Then,the proposed deep network is trained by using multi-source data sets such as road cracks,wall cracks and bridge cracks.Based on the transfer learning idea,the learned knowledge is transferred to the target domain model of dam crack to further improve the detection accuracy.Finally,a multi-model fusion method is proposed to integrate the detection results of different models obtained through transfer learning,which can effectively enhance the location of output boxes.

Key words: Dam crack detection, Deep learning, Model fusion, Transfer learning

CLC Number: 

  • TP391
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