Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, People’s Republic of China

Research Article

Defect Detection and Recognition of Mobile Phone Membrane Based on Convolutional Neural Network

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  • @INPROCEEDINGS{10.4108/eai.6-6-2021.2307711,
        author={Changmao  Li and Enbo  Zhang and Li  Liu},
        title={Defect Detection and Recognition of Mobile Phone Membrane Based on Convolutional Neural Network},
        proceedings={Proceedings of the 8th EAI International Conference on Green Energy and Networking, GreeNets 2021, June 6-7, 2021, Dalian, People’s Republic of China},
        publisher={EAI},
        proceedings_a={GREENETS},
        year={2021},
        month={8},
        keywords={mobile phone film target location convolutional neural network defect detection},
        doi={10.4108/eai.6-6-2021.2307711}
    }
    
  • Changmao Li
    Enbo Zhang
    Li Liu
    Year: 2021
    Defect Detection and Recognition of Mobile Phone Membrane Based on Convolutional Neural Network
    GREENETS
    EAI
    DOI: 10.4108/eai.6-6-2021.2307711
Changmao Li1, Enbo Zhang1, Li Liu1,*
  • 1: Department of Information Science and Engineering Dalian Polytechnic University Dalian, P. R. China
*Contact email: link_liuli@hotmail.com

Abstract

With the upgrading of mobile phone equipment, automatic detection of mobile phone film defects has been paid more and more attention in industrial production quality. Mobile phone film defect detection is a huge workload and challenging problem. Traditional methods can also detect some industrial identification defects, but these methods can only detect defects under specific conditions, such as obvious defect outline, strong contrast, low noise conditions. The defect detection method of mobile phone film proposed in this paper is to locate the target area with input images obtained from the industrial environment, remove the background, and then classify them into their designated classes through convolutional neural network. Experimental results show that this method can meet the robustness and accuracy of mobile phone film defect detection.