Performance evaluation of liquid pouch inspection using conventional deep learning models

Reo Tokuke; Hidenori Kogure; Takeshi Kanemoto; Makoto Hasegawa

doi:10.1117/12.2589512

13 March 2021 Performance evaluation of liquid pouch inspection using conventional deep learning models

Reo Tokuke, Hidenori Kogure, Takeshi Kanemoto, Makoto Hasegawa

Proceedings Volume 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021; 117662V (2021) https://doi.org/10.1117/12.2589512
Event: International Workshop on Advanced Imaging Technology 2021 (IWAIT 2021), 2021, Online Only

Abstract

The performance of conventional deep learning models for liquid pouch inspection was analyzed. Pictures of liquid pouches were captured using soft X-ray transmission to generate our training, evaluation, and test datasets that included defective and defect-free products. Nine conventional deep learning models were trained using the training dataset, and their accuracy was computed using the evaluation and test datasets; ResNet50 and MobileNet demonstrated better performance. Because the difference between our defective and defect-free images was small, the learning effect was difficult to propagate to the deep layers. Therefore, the skip connections were indispensable to realize a deep layer for our application. While changing the discrimination threshold, it was possible to detect all defective products by allowing 5.59 % defect-free products to be mistakenly made defective in our experiments.

Citation Download Citation

Reo Tokuke, Hidenori Kogure, Takeshi Kanemoto, and Makoto Hasegawa "Performance evaluation of liquid pouch inspection using conventional deep learning models", Proc. SPIE 11766, International Workshop on Advanced Imaging Technology (IWAIT) 2021, 117662V (13 March 2021); https://doi.org/10.1117/12.2589512

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