基于多尺度卷积神经网络的缺陷红枣检测方法

提出了一种基于多尺度卷积神经网络的缺陷红枣检测方法,在AlexNet卷积神经网络上增加并行的多尺度卷积模块,增加网络的深度和宽度,减少网络中的参数;在卷积层中加入批标准化处理,减少训练过程中数据分布的变化,提高网络的泛化能力。以新疆干制红枣中的黄皮枣、霉变枣、破头枣和正常枣为研究对象,对这些干制红枣进行训练和验证。结果表明:该模型对黄皮枣、霉变枣、破头枣和正常枣的识别率分别为96.67%,96.25%,98.57%,97.14%,综合识别率可达97.14%。与其他的算法相比,该算法具有较强的稳健性,对缺陷红枣的识别准确率更高。

Defective jujube detection technology based on multi-scale convolutional neural network

In this paper,a method based on a multi-scale convolutional neural network for detecting defects in jujube is proposed.Parallel multi-scale convolution modules were added to the AlexNet convolutional neural network to increase the depth and width of the network and reduce the parameters in the network;Added batch normalization processing to the convolutional layer to reduce changes in data distribution during training and improve the generalization ability of the network.Taking the yellow-skinned jujube,moldy jujube,broken-head jujube and normal jujube in Xinjiang dried jujube as the research objects,these dried jujubes were trained and verified.The results showed that the recognition rates of this model for yellow-skinned jujubes,moldy jujubes,broken-head jujubes and normal jujubes were 96.67%,96.25%,98.57%,and 97.14%respectively,and the comprehensive recognition rate could reach 97.14%.Compared with other algorithms,this algorithm was more robust and had higher accuracy in identifying defective red jujubes.