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Automatic Defect Detection for Small Metal Cylindrical Shell Using Transfer Learning and Logistic Regression

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Abstract

Since small metal cylindrical shell (MCS) is a kind of very important metal object widely used in structure engineering and weapon production, especially in the manufacture of bullets, it is necessary to assure the high-precision surface of MCS. While the detection of MCS is generally done manually. In this paper, a novel automatic defect detection system for MCS is built using transfer learning of Inception-v3 and logistic regression (LR). By using the powerful feature extraction capabilities of Inception-v3 deep convolutional neural network, features are fetched from MCS images firstly and then trained on an LR machine learning classifier to establish a detection model. During the process of detection, five images of one MCS captured by the camera are sent to the computer for detection using the established detection model, with these five images’ composite outcomes representing this MCS’s detection result. Experimental results show that the proposed detection system could reach an accuracy of 97%, meeting the requirements of industrial production.

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Acknowledgements

This work was supported in part by the Chongqing Changjiang Electrical Industry Co., Ltd. Besides, this work was also supported by the Research on Intelligent Recognition Technology of Spacecraft Composite Defects funded by the Shanghai Institute of Satellite Equipment (No. JG20180209).

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Authors and Affiliations

  1. Key Laboratory of Optoelectronic Technology and System of the Ministry of Education, Chongqing University, Chongqing, 400030, China

    Yanfeng Gong, Jun Luo, Keren He & Wei Zeng

  2. Shanghai Institute of Satellite Equipment, Shanghai, 201100, China

    Hongliang Shao

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  1. Yanfeng Gong
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  2. Jun Luo
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  3. Hongliang Shao
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  4. Keren He
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  5. Wei Zeng
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Correspondence to Jun Luo.

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Gong, Y., Luo, J., Shao, H. et al. Automatic Defect Detection for Small Metal Cylindrical Shell Using Transfer Learning and Logistic Regression. J Nondestruct Eval 39, 24 (2020). https://doi.org/10.1007/s10921-020-0668-4

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