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


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



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


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.


S. Careena, D. Sani, S. N. Tan, C. W. Lim, S. Hassan, M. Norhafizah, et al., “Effect of Edible Bird’s Nest Extract on Lipopolysaccharide-Induced Impairment of Learning and Memory in Wistar Rats,” Evidence-Based Complementary and Alternative Medicine, vol. 2018, 9318789, August 2018.

G. K. L. Chan, Z. Wong, K. Lam, L. Cheng, L. Zhang, H. Lin, et al., “Edible Bird’s Nest, an Asian Health Food Supplement, Possesses Skin Lightening Activities: Identification of N-Acetylneuraminic Acid as Active Ingredient,” Journal of Cosmetics, Dermatological Sciences and Applications, vol. 5, no. 4, pp. 262-274, January 2015.

C. T. Guo, T. Takahashi, W. Bukawa, N. Takahashi, H. Yagi, K. Kato, et al., “Edible Bird's Nest Extract Inhibits Influenza Virus Infection,” Antiviral Reseach, vol. 70, no. 3, pp. 140-146, July 2006.

G. K. Meng, L. W. Kin, T. P. Han, D. Koe, and W. J. K. Raymond, “Size Characterisation of Edible Bird Nest Impurities: A Preliminary Study,” Procedia Computer Science, vol. 112, pp. 1072-1081, September 2017.

Y. Subramaniam, Y. C. Fai, and E. S. L. Ming, “Edible Bird Nest Processing Using Machine Vision and Robotic Arm,” Jurnal Teknologi, vol. 72, no. 2, pp. 85-88, 2015.

C. K. Yee, Y. H. Yeo, L. H. Cheng, and K. S. Yen, “Impurities Detection in Edible Bird’s Nest Using Optical Segmentation and Image Fusion,” Machine Vision and Applications, vol. 31, no. 7, November 2020.

Malaysia, International Law Book Service. Legal Research Board, Food Act 1983 (Act 281); & Food Regulations 1985: as at 25th July 1994, Kuala Lumpur: International Law Book Services, 1994.

H. Min, W. Jia, X. F. Wang, Y. Zhao, and Y. T. Luo, “A Polynomial Piecewise Constant Approximation Method Based on Dual Constraint Relaxation for Segmenting Images with Intensity Inhomogeneity,” Pattern Recognition, vol. 73, pp. 15-32, January 2018.

Y. Peng and C. Xiao, “An Oriented Derivative of Stick Filter and Post-Processing Segmentation Algorithms for Pulmonary Fissure Detection in CT Images,” Biomedical Signal Processing and Control, vol. 43, pp. 278-288, May 2018.

M. M. George and S. Kalaivani, “Retrospective Correction of Intensity Inhomogeneity with Sparsity Constraints in Transform-Domain: Application to Brain MRI,” Magnetic Resonance Imaging, vol. 61, pp. 207-223, September 2019.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436-444, May 2015.

Y. Guo, Ü. Budak, L. J. Vespa, E. Khorasani, and A. Şengür, “A Retinal Vessel Detection Approach Using Convolution Neural Network with Reinforcement Sample Learning Strategy,” Measurement, vol. 125, pp. 586-591, September 2018.

N. D. Hoang, Q. L. Nguyen, and V. D. Tran, “Automatic Recognition of Asphalt Pavement Cracks Using Metaheuristic Optimized Edge Detection Algorithms and Convolution Neural Network,” Automation in Construction, vol. 94, pp. 203-213, October 2018.

L. Hou, V. Nguyen, A. B. Kanevsky, D. Samaras, T. M. Kurc, T. Zhao, et al., “Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images,” Pattern Recognition, vol. 86, pp. 188-200, February 2019.

S. Mei, Y. Wang, and G. Wen, “Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model,” Sensors, vol. 18, no. 4, 1064, April 2018.

X. Guo, L. H. Schwartz, and B. Zhao, “Automatic Liver Segmentation by Integrating Fully Convolutional Networks into Active Contour Models,” Medical Physics, vol. 46, no. 10, pp. 4455-4469, October 2019.

Z. Liu, Y. Cao, Y. Wang, and W. Wang, “Computer Vision-Based Concrete Crack Detection Using U-Net Fully Convolutional Networks,” Automation in Construction, vol. 104, pp. 129-139, August 2019.

H. Han, C. Gao, Y. Zhao, S. Liao, L. Tang, and X. Li, “Polycrystalline Silicon Wafer Defect Segmentation Based on Deep Convolutional Neural Networks,” Pattern Recognition Letters, vol. 130, pp. 234-241, February 2020.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” International Conference on Medical Image Computing and Computer-Assisted Intervention, October 2015, pp. 234-241.

G. L. Howett, “Size of Letters Required for Visibility as a Function of Viewing Distance and Observer Visual Acuity,” U.S. Department of Commerce, National Bureau of Standards, NBS Technical Note 1180, July 1983.

K. L. Gwee, L. H. Cheng, and K. S. Yen, “Optimization of Lighting Parameters to Improve Visibility of Impurities in Edible Bird’s Nest,” Journal of Electronic Imaging, vol. 28, no.2, 023014, March 2019.

Good Manufacturing Practice (GMP) for Processing Raw-Unclean and Raw-Clean Edible-Bird Nest (EBN), Malaysian Standard MS 2333:2010, 2010.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 1st ed. Cambridge: MIT press, 2016.

P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis,” Seventh International Conference on Document Analysis and Recognition, August 2003, pp. 958-963.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” International Conference on Learning Representations, May 2015.

B. T. Polyak, “Some Methods of Speeding up the Convergence of Iteration Methods,” USSR Computational Mathematics and Mathematical Physics, vol. 4, no. 5, pp. 1-17, 1964.

G. Hinton, N. Srivastava, and K. Swersky, “Neural Networks for Machine Learning,”, 2012.

M. Kuhn and K. Johnson, Applied Predictive Modeling, 1st ed. New York: Springer, 2013.

G. Montavon, G. Orr, and K. R. Müller, Neural Networks: Tricks of the Trade, 2nd ed. Berlin: Springer, 2012.

J. D. Rodriguez, A. Perez, and J. A. Lozano, “Sensitivity Analysis of K-Fold Cross Validation in Prediction Error Estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 569-575, March 2010.

S. J. Miller, “The Method of Least Squares,” Mathematics Department Brown University, vol. 8, pp. 1-7, 2006.

A. Tharwat, “Classification Assessment Methods,” Applied Computing and Informatics, vol. 16, pp. 1-25, August 2020.

L. R. Dice, “Measures of the Amount of Ecologic Association Between Species,” Ecology, vol. 26, no. 3, pp. 297-302, July 1945.

B. Guindon and Y. Zhang, “Application of the Dice Coefficient to Accuracy Assessment of Object-Based Image Classification,” Canadian Journal of Remote Sensing, vol. 43, no. 1, pp. 48-61, January 2017.




How to Cite

Ying-Heng Yeo and Kin-Sam Yen, “Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model”, Int. j. eng. technol. innov., vol. 11, no. 2, pp. 135–145, Apr. 2021.