单 位：
华中农业大学工学院;国家蛋品研发中心;农业农村部长江中下游农业装备重点实验室
关键词：
种鸭蛋;受精;深度学习;无损检测;图像识别
摘 要：
针对我国鸭蛋孵化行业剔除无精蛋的方法效率低、剔除的无精蛋已丧失食用价值、造成资源巨大浪费的问题,运用机器视觉技术,以孵化至第3天的种鸭蛋为研究对象,运用深度卷积神经网络(Convolutional neural networks,CNN)端对端的特点,在Alexnet神经网络基础上进行改进,将孵化第3天的种鸭蛋透射图像直接输入到深度卷积神经网络。用卷积层代替全连接层,改变卷积核的尺寸,搭建了种鸭蛋受精信息识别网络(Eggnet)模型,实现了对种鸭蛋孵化早期受精信息的无损判别。试验结果表明,该方法对孵化第3天的种鸭蛋图像测试集分类准确率高达98. 87%,验证集分类准确率为97. 97%,平均单枚蛋检测时间仅为0. 24 s。
译 名：
Non-destructive Testing of Early Fertilization Information in Duck Egg Laying Based on Deep Learning
作 者：
LI Qingxu;WANG Qiaohua;GU Wei;SHI Hang;MA Meihu;College of Engineering,Huazhong Agricultural University;Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River,Ministry of Agriculture and Rural Agriculture;National Egg Research and Development Center;
关键词：
duck eggs;;fertilization;;deep learning;;non-destructive testing;;image recognition
摘 要：
China is a big country in the production of duck eggs and ducks. The duck egg hatching industry has a huge output. It needs to incubate billions of ducklings every year to meet the production needs. At present,the method of removing the infertile eggs in the duck egg hatching industry in China is to visually recognize the eggs by artificially photographing the eggs about 7 days after hatching. This method is inefficient and has no edible value after 7 days of incubation,which will cause huge waste of resources. Machine vision technology was used to hatch the third day of the duck eggs. The end-to-end characteristics of the deep convolutional neural network was used to the image of the duck egg on the third day of incubation,and it was directly input into the neural network,and the Alexnet neural network. The convolutional layer was used to replace the fully connected layer,and the size of the convolution kernel was changed. An egg net fertilization information recognition network( Eggnet) model was established to realize the non-destructive discrimination of the fertilization information in the early hatching of the duck eggs. The test results showed that the accuracy rate of the method for the classification of the duck eggs in the third day of hatching was as high as 98. 87%,the accuracy of the verification set was 97. 97%,and the average single egg detection time was only 0. 24 s. This technology can be used in the actual production of duck egg hatching industry in the later stage. It would replace the artificial egg method to select the infertile egg. It can solve the problem of automatic device installation in the egg hatching industry. It had broad application space.
