Fertility Detection of Middle-stage Hatching Egg in Vaccine Production Using Machine Vision

Shan, Baoming

doi:10.1109/etcs.2010.540

Cited by 13 publications

(11 citation statements)

References 4 publications

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“…Waktu pelaksanaan proses candling dilakukan dalam beberapa tahap untuk mengetahui kesuburan telur dan perkembangan embrio ayam (Bamelis, dkk., 2002). Waktu yang dilakukan untuk melakukan candling dengan tujuan mengetahui kesuburan telur dilakukan pada hari ke-3 sampai ke-6 proses inkubasi (inkubasi dengan mesin tetas) dengan melihat nokta merah pada telur, sedangkan waktu candling pada hari ke-12 sampai ke-18 proses inkubasi dilakukan untuk melihat pertumbuhan ataupun kematian dari embrio (Lawrence, dkk., 2006) dan (Shan, 2010).…”

Section: Pendahuluanunclassified

Kombinasi Fitur Bentuk, Warna dan Tekstur untuk Identifikasi Kesuburan Telur Ayam Kampung Sebelum Inkubasi

Dijaya¹,

Suciati²,

Herumurti³

2016

JBI

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Section: Pendahuluanunclassified

Kombinasi Fitur Bentuk, Warna dan Tekstur untuk Identifikasi Kesuburan Telur Ayam Kampung Sebelum Inkubasi

Dijaya¹,

Suciati²,

Herumurti³

2016

JBI

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“…Then, the percentage of the vascular region was calculated to determine the activity of the embryonic eggs. 8 A weight fuzzy C-means algorithm was also used by Shan 9 for adaptive segmentation and to extract the major vascular information. Despite the success of these methods mentioned above, these technologies are either destructive or based on traditionally complicated image processing, such as image enhancement, image segmentation, etc.…”

Section: Introductionmentioning

confidence: 99%

DPSA: dense pixelwise spatial attention network for hatching egg fertility detection

Geng

Xiao

et al. 2020

J. Electron. Imag.

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© 2020 SPIE and IS & T. Deep convolutional neural networks show a good prospect in the fertility detection and classification of specific pathogen-free hatching egg embryos in the production of avian influenza vaccine, and our previous work has mainly investigated three factors of networks to push performance: depth, width, and cardinality. However, an important problem that feeble embryos with weak blood vessels interfering with the classification of resilient fertile ones remains. Inspired by fine-grained classification, we introduce the attention mechanism into our model by proposing a dense pixelwise spatial attention module combined with the existing channel attention through depthwise separable convolutions to further enhance the network class-discriminative ability. In our fused attention module, depthwise convolutions are used for channel-specific features learning, and dilated convolutions with different sampling rates are adopted to capture spatial multiscale context and preserve rich detail, which can maintain high resolution and increase receptive fields simultaneously. The attention mask with strong semantic information generated by aggregating outputs of the spatial pyramid dilated convolution is broadcasted to low-level features via elementwise multiplications, serving as a feature selector to emphasize informative features and suppress less useful ones. A series of experiments conducted on our hatching egg dataset show that our attention network achieves a lower misjudgment rate on weak embryos and a more stable accuracy, which is up to 98.3% and 99.1% on 5-day and 9-day old eggs, respectively.

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“…Hatching eggs activity detection is a method that aims to classify live embryos and dead embryos accurately and quickly. Over the past decades, a variety of classification methods have been developed to improve embryo feature classification, including bioelectrical detection [1], acoustic resonance [2], [3], ultrasonographic imaging [4], thermal difference [5], hyperspectral imaging [6]- [9], and machine vision [10], [11]. Among these methods, hyperspectral imaging and machine vision achieved 93% and 97.78% classification accuracy, respectively.…”

Section: Introductionmentioning

confidence: 99%

Fully Convolutional Network With Gated Recurrent Unit for Hatching Egg Activity Classification

et al. 2019

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A hatching egg activity classification method aims to accurately and quickly distinguish between dead embryos and live embryos. The existing embryonic classification models collect egg images via a specific imaging system. The image features are then extracted to identify and classify the properties of the hatching eggs. The current state-of-the-art embryonic image classification methods are easily affected by the image quality and are not efficient. To address these issues, we propose a new classification model based on fully convolutional networks (FCNs) and a gated recurrent unit (GRU) that decides whether an embryo is dead or alive by determining embryotic heartbeat signal indicators. Our dataset consists of heartbeat signals from 50k distinct chicken embryos. The experimental results based on our dataset show that our proposed model is the most accurate compared with all baseline models. The reason for this is that our model can capture more useful information from heartbeat signals. In addition, our model can classify 83 hatching eggs per second. INDEX TERMS Deep learning, hatching eggs classification, pattern recognition.

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Fertility Detection of Middle-stage Hatching Egg in Vaccine Production Using Machine Vision

Cited by 13 publications

References 4 publications

Kombinasi Fitur Bentuk, Warna dan Tekstur untuk Identifikasi Kesuburan Telur Ayam Kampung Sebelum Inkubasi

Kombinasi Fitur Bentuk, Warna dan Tekstur untuk Identifikasi Kesuburan Telur Ayam Kampung Sebelum Inkubasi

DPSA: dense pixelwise spatial attention network for hatching egg fertility detection

Fully Convolutional Network With Gated Recurrent Unit for Hatching Egg Activity Classification

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