Impurities detection in edible bird’s nest using optical segmentation and image fusion

Yee, Cong Kai; Yeo, Ying Heng; Cheng, Lai-Hoong; Yen, Kuo‐Yang

doi:10.1007/s00138-020-01124-y

Cited by 6 publications

(14 citation statements)

References 11 publications

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“…The misclassification rate therefore could be calculated as 13.05%, a summation of false-negative and false-positive. The true-positive rate recorded in this study (96.69%) was higher than that achieved in Subramaniam's [5] work (68.18% to 87.93%) and Yee's [6] work (93.39%). In addition, the U-net based EBN impurities detection algorithm was able to score an improved misclassification rate (13.05%), far lower than the recorded values of 57% in Goh's [4] work and 32.25% in Yee's work.…”

Section: Resultscontrasting

confidence: 72%

“…This phenomenon was attributed to the difference in the penetration power of the lights with various wavelengths and optical phenomena in different mediums [21]. Therefore, different information could be retrieved from the images captured under different lights to facilitate the EBN impurities detection [6]. To retrieve this information, the EBN sample images were captured under various colored lights, namely red, green, blue, and white lights provided by TMS Lite LBRQ 3 Rows Series.…”

Section: Image Acquisition and Dataset Preparationmentioning

confidence: 99%

“…Several researches [4][5][6] have attempted to investigate automatic cleanliness inspection; however, the results were not satisfactory. Subramaniam et al [5] performed EBN impurities detection on raw EBN soaked in water.…”

Section: Introductionmentioning

confidence: 99%

“…The misclassification rate was recorded at 57%. With proposed optical segmentation and image fusion techniques, Yee et al [6] demonstrated that the recall/true positive rate of the impurities detection fetched as high as 93% out of 980 impurities. Even though the result was encouraging, a high misclassification rate was recorded at 32.25%.…”

Section: Introductionmentioning

confidence: 99%

“…The natural translucency of the EBN allows illuminated light to be transmitted, absorbed and reflected at different degrees, thus resulting in varying and highly inconsistent intensities of EBN [8] across the EBN region. The irregularities in shape, density, and thickness [6] of the entangled EBN strands further complicates the EBN image processing and analysis. Although researchers have developed various image processing and analysis methods [8][9][10] to reduce the effects of intensity inhomogeneity in other domains, these methods are application-specific and can only be used in certain domains of problems.…”

Section: Introductionmentioning

confidence: 99%

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

Yeo

Yen

2021

Int. j. eng. technol. innov.

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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.

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Section: Resultscontrasting

confidence: 72%

Section: Image Acquisition and Dataset Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yeo

Yen

2021

Int. j. eng. technol. innov.

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Recent advancement of intelligent-systems in edible birds nest: A review from production to processing

Goh,

Lim,

Krishnamoorthy

et al. 2023

Multimed Tools Appl

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Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

Shuyuti,

Salami,

Dahari

et al. 2024

IEEE Access

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With the rapid development of artificial intelligence (AI), especially in machine learning and deep learning technologies, the particle and impurity detection and removal processes employed many industries have been improved. Particles and impurities at any size, shape and at any condition can be detected using the advanced technology in both areas. This paper presents a comprehensive overview of research papers that discuss the application of AI techniques for the detection and removal of particles and impurities. The publications featured in this review were mainly retrieved from the Web of Science (WoS) database, covering the timeframe from 2000 to 2023. This paper also covers the review on the impurity detection and removal specifically in edible bird's nest (EBN). The aim of this paper is to provide a valuable resource for the future development of AI applications in particle and impurity detection and removal technologies that have not been addressed in this study. Through the review and analysis of AI for particle and impurity detection and removal techniques in recent years, this paper includes the following parts: research trend in particle and impurity detection in general and AI in particle and impurity detection, applications of AI methods in particle and impurity detection in related industries including in EBN and AI application in particle and impurity removal. This review study will offer advantages to researchers engaged in the field of AI with regards to the detection and removal of particles and impurities.

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Impurities detection in edible bird’s nest using optical segmentation and image fusion

Cited by 6 publications

References 11 publications

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

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

Recent advancement of intelligent-systems in edible birds nest: A review from production to processing

Application of Artificial Intelligence in Particle and Impurity Detection and Removal: A Survey

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