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基于原型網絡的小樣本禽蛋圖像特征檢測方法
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國家自然科學基金面上項目(31871863)和湖北省重點研發(fā)計劃項目(2020BBB072)


Feature Detection Method of Small Sample Poultry Egg Image Based on Prototypical Network
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    摘要:

    機器視覺因具有檢測速度快,、穩(wěn)定性高及成本低等優(yōu)點,,已發(fā)展成為禽蛋無損檢測領域主流檢測手段,。使用該技術對禽蛋進行無損檢測時,,需要依賴大量禽蛋圖像作為數(shù)據支撐才能取得較好的檢測效果,。由于養(yǎng)殖安全等限制,,禽蛋圖像數(shù)據的采集成本較高,,針對該問題,,提出了一種適應于小樣本禽蛋圖像檢測的原型網絡(Prototypical network)。該網絡利用引入注意力機制的逆殘差結構搭建的卷積神經網絡將不同類別的禽蛋圖像映射至嵌入空間,,并利用歐氏距離度量測試禽蛋圖像在嵌入空間的類別,,從而完成禽蛋圖像的分類。本文利用該網絡分別驗證了小樣本條件下受精蛋與無精蛋,、雙黃蛋與單黃蛋及裂紋蛋與正常蛋的分類檢測效果,,其檢測精度分別為95%、98%,、88%,。試驗結果表明本文方法能夠有效地解決禽蛋圖像檢測中樣本不足的問題,為禽蛋圖像無損檢測研究提供了新的思路,。

    Abstract:

    Machine vision has developed into a mainstream testing method in the field of nondestructive testing of poultry eggs due to its advantages such as high detection speed, high stability and low cost. A large-number of egg images are often used as data support to achieve better detection results. However, the collection cost of egg image data is relatively high,,and it costs a lot of manpower and material resources. Therefore, it is hoped to find a method similar to face recognition for small sample egg image detection. To solve this problem, a prototypical network suitable for the detection of small sample egg images was proposed. The network used the inverse residual structure of attention-introducing mechanism to build a convolutional neural network to map different types of egg images to the embedded space, and Euclidean distance measurement was used to test the types of egg images in the embedded space, so as to complete the classification of egg images. The network was used to verify the classification detection effect of fertilized egg and unfertilized egg, double yolk egg and single yolk egg, cracked egg and normal egg under the condition of small sample. Its detection accuracy was 95%, 98%, 88%, respectively. The test results showed that the method effectively solved the problem of insufficient samples in the detection of poultry egg image, and provided an idea for the research of nondestructive detection of poultry egg image. In future nondestructive testing of poultry egg images, a small amount of poultry egg images can be collected to achieve better detection results.

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李慶旭,王巧華.基于原型網絡的小樣本禽蛋圖像特征檢測方法[J].農業(yè)機械學報,2021,52(11):376-383. LI Qingxu, WANG Qiaohua. Feature Detection Method of Small Sample Poultry Egg Image Based on Prototypical Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(11):376-383.

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  • 收稿日期:2020-12-02
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  • 在線發(fā)布日期: 2021-11-10
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