Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model

Ying-Heng Yeo; Kin-Sam Yen

doi:10.46604/ijeti.2021.6891

Authors

Ying-Heng Yeo School of Mechanical Engineering, Universiti Sains Malaysia, Penang, Malaysia
Kin-Sam Yen School of Mechanical Engineering, Universiti Sains Malaysia, Penang, Malaysia

DOI:

https://doi.org/10.46604/ijeti.2021.6891

Keywords:

edible bird’s nest, impurities detection, intensity inhomogeneity, U-net, machine vision

Abstract

As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate.

Author Biography

Ying-Heng Yeo, School of Mechanical Engineering, Universiti Sains Malaysia, Penang, Malaysia

Ying Heng Yeo is a post-graduate student who currently pursues Degree of M.Sc. (Mechanical Engineering) at Universiti Sains Malaysia. He received his Bachelor of Mechanical Engineering degree from Universiti Sains Malaysia in 2019. His current research interests include artificial intelligence and machine vision.

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