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Convolutional Neural Network Based Surface Inspection System for Non-patterned Welding Defects

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Abstract

In this paper, we propose a convolutional neural network (CNN) based method that inspects non-patterned welding defects (craters, pores, foreign substances and fissures) on the surface of the engine transmission using a single RGB camera. The proposed method consists of two steps: first, extracting the welding area to be inspected from the captured image, and then determining whether the extracted area includes defects. In the first step, to extract the welding area from the captured image, a CNN based approach is proposed to detect a center of the engine transmission in the image. In the second stage, the extracted area is identified by another CNN as defective or non-defective. To train the second stage CNN stably, we propose a class-specific batch sampling method. With our sampling method, biased learning caused by data imbalance (number of collected defective images is much less than that of non-defective images) is effectively prevented. We evaluated our system with a large amount of samples (about 32,000 images) collected manually from the production line, and our system shows a remarkable performance in all experiments.

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Abbreviations

i :

Input nodes of layer

o :

Output nodes of layer

w :

Learnable weights of layer

b :

Bias of layer

\({\mathcal{F}}\) :

Activation function

c :

Estimated center of the engine transmission

\(\hat{c}\) :

Ground truth center of the engine transmission

N :

Number of training samples in a batch

r 1 :

Lower radius of the welding area

r 2 :

Upper radius of the welding area

θ 1 :

Lower angle of the welding area

θ 2 :

Upper angle of the welding area

p :

Probability distribution of the estimated class

\(\hat{p}\) :

Probability distribution of the ground truth class

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) (No. 2016R1A2B4007608) and National IT Industry Promotion Agency (NIPA) Grant funded by the Korea government (MSIT) (No. S0602-17-1001) and partly supported by Cooperative R&D fund of Korea Ministry of SMEs and Startups (S2605414).

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Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, 2, Busandaehak-ro, 63beon-gil, Geumjeong-gu, Busan, 46241, South Korea

    Je-Kang Park & Dong-Joong Kang

  2. Transmission Production Engineering Team 1 - Production and Development Division, Hyundai Motor Company, 700 Yeompo-ro, Buk-Gu, Ulsan, 44259, South Korea

    Woo-Hyun An

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  1. Je-Kang Park
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Correspondence to Dong-Joong Kang.

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Park, JK., An, WH. & Kang, DJ. Convolutional Neural Network Based Surface Inspection System for Non-patterned Welding Defects. Int. J. Precis. Eng. Manuf. 20, 363–374 (2019). https://doi.org/10.1007/s12541-019-00074-4

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