基于自适应卷积神经网络的染病虾识别方法

针对南美白对虾样本来源多样导致的泛化效果较差的问题，引入香农信息论构造不同来源样本的特征差异模型，以深度卷积神经网络(DCNN)为识别框架基础，依据多源样本组成的数据集在分类前后呈现的熵减规则计算DCNN中的网络超参数，消解数据集从随机输入到规则输出的信息熵，打破数据类型从三维输入到一维输出的熵变动，实现图像数据由高维空间向低维空间的映射，获取DCNN中关于超参数和网络深度的自适应优化策略，以提高识别不同来源染病虾的泛化效果。实验结果表明，所提方法在单个数据集上的识别精度最高可达97.96%,并在其他4个图像数据集上进行了测试泛化，泛化精度下降幅度小于5个百分点。

Diseased Shrimp Identification Method Based on Adaptive Convolutional Neural Networks

To solve the problem of weak generalization caused by diversity of source of shrimp samples, a novel shrimp features difference model based on shannon information theory was proposed. The model was actually a recognition framework, calculating hyper-parameters based on deep convolutional neural network (DCNN) using entropy reduction rule with multi-source datasets. This rule can clear up the special information entropy from the random input to regular output, breaking the data types changing from three dimensional input to one-dimensional output, realizing dimensionality reduction of shrimp image reducing from high dimension space to low dimensional space. Thus, the DCNN adaptive optimization strategies can be acquired to improve the generlization effectiveness of recognizing diseased shrimp from multiple sources. The experimental results showed that the proposed method in a single dataset can achieve highest accuracy of 97.96%. The generalization experiment was also tested through other four shrimp image datasets, and the generalization precision falling scope was no more than 5 percentage points.