Computer Science ›› 2016, Vol. 43 ›› Issue (2): 64-67.doi: 10.11896/j.issn.1002-137X.2016.02.014

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Capsule Defects Detection Based on Stacked Denoising Autoencoders

WANG Xian-bao, HE Wen-xiu, WANG Xin-gang, YAO Ming-hai and QIAN Yun-tao   

  • Online:2018-12-01 Published:2018-12-01

Abstract: At present defects of capsules are detected mainly by manual operation,which is time-consuming and needs high labor costs,besides,it is easily misled by subjective factors.This paper proposed a method of detection of capsules surface defects based on stacked denoising autoencoders (SDAE).Our method firstly establishes deep autoencoders networks and trains using a denoising criterion according to the defect samples to obtain the initial weights at first.Then,BP algorithm fine-tunes the network parameters to get the mapping relationship between the training sample and defect-free template.Finally, defect detection of the testing samples is finished by comparing the reconstruction image and defect image.Experimental results show that SDAE perfectly establishes the mapping relationship,which is robust and stable to noise,and can quickly detect defects with high accuracy.

Key words: Stacked denoising autoencoders,Defect detection,Deep learning

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