Research Article
Defect Detection and Recognition of Mobile Phone Membrane Based on Convolutional Neural Network
@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
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.
