Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (7): 92-96.doi: 10.11896/jsjkx.190700093

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Injection-molded Bottle Defect Detection Using Semi-supervised Deep Convolutional Generative Adversarial Network

XIE Yuan, MIAO Yu-bin, XU Feng-lin, ZHANG Ming   

  1. School of Mechanical Engineering,Shanghai Jiaotong University,Shanghai 200240,China
  • Received:2019-07-13 Online:2020-07-15 Published:2020-07-16
  • About author:XIE Yuan,born in 1997,postgraduate.His main research interests include machine learning and control algorithms.
    MIAO Yu-bin,born in 1973,Ph.D,associate professor.His main interests include intelligent devices,smart sensors,and 3-dimentional image processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (51975361)

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Abstract: Defect detection of injection-molded bottles is an important part of injection molding.Due to the relatively few defective samples in production,the model tends to over-fit when using deep learning algorithm.In order to solve this problem,a defect detection model based on semi-supervised deep convolutional generative adversarial network(DCGAN) is proposed.Firstly,the model preprocesses the original images using HSV color space transformation and Otsu threshold segmentation methods.Then,the learning tasks are combined so that the unsupervised discriminator and the supervised classifier share convolutional parameters.At the same time,the loss function is modified,which consists of cross entropy and wasserstein distance.Finally,the model is fine-tuned using Adam optimizer.The experimental results show that the model can distinguish the defective samples,achieving an accuracy of 98.65%.Compared with traditional machine learning algorithm and CNN model with data augmentation,the proposed model avoids over-fitting.

Key words: Deep convolutional generative adversarial network, Defect detection, Injection-molded bottle, Semi-supervised, Small-sized datasets

CLC Number: 

  • TP277
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