基于深度学习的鸡种蛋早期受精信息无损检测

NON-DESTRUCTIVE DETECTION OF EARLY FERTILIZATION INFORMATION OF CHICKEN EGGS BASED ON DEEP LEARNING

  • 摘要: 针对当前人工照蛋检测无精蛋时间晚、工作强度大等问题,以入孵2.5天鸡种蛋为研究对象,改进VGG16网络模型并开发图形用户界面,利用自制的静态图像采集装置采集入孵2.5天鸡种蛋图像。改进后的模型在增强后的测试集上判别准确率为98.82%,召回率为97.23%,单幅图像检测耗时为97.56ms,与原网络相比,识别精度提升5.56百分点,单幅图像识别时间节省14.78ms。研究结果表明,改进后的模型能有效实现孵化早期鸡种蛋受精信息的无损鉴别,为后续研发在线无损检测装置提供技术支持。

     

    Abstract: In order to solve the problems of late time and high work intensity in the detection of eggs without sperm, a VGG16 network model was improved and a graphical user interface was developed for hatching 2.5 d eggs. The image of hatching 2.5 d eggs was collected by a self-made static image acquisition device. The improved model achieved 98.82% discrimination accuracy and 97.23% recall rate on the enhanced test set, and the detection time of single image was 97.56 ms. Compared with the original network, the recognition accuracy was improved by 5.56 percentage points, and the recognition time of single image was saved by 14.78 ms. The results show that the improved model can effectively realize the nondestructive identification of egg fertilization information in the early stage of incubation, which provides technical support for the subsequent development of online nondestructive testing devices.

     

/

返回文章
返回