基于多组卷积神经网络的梭子蟹性别识别研究

Multi-group convolutional neural network for gender recognition of Portunus tritubereulatus

为了实现梭子蟹的智能化分拣,高精度的智能识别分类成为亟待开发的关键技术。首先对采集到的梭子蟹图像进行预处理和数据增强,构建出梭子蟹性别分类数据集(Portunus gender classification dataset,PGCD);提出一种基于多组卷积神经网络的梭子蟹性别识别方法,该方法主要使用ResNet50从图像块中提取特征,降低特征提取过程的信息损失。为了更专注地找出输入数据的有用信息,开发出一种注意力机制来强调全局特征图中的细节重要性;最后进行了一系列的参数调整,提高了网络的训练效率和分类精度。实验结果显示,该方法在PGCD上的分类准确率、召回率和查准率分别达到95.59%、94.41%和96.68%,识别错误率仅为4.41%。表明该方法具有优越的分类性能,可用于梭子蟹性别的自动分类及识别系统。

High-precision intelligent recognition and classification has become a key technology for intelligent sorting of Portunus trituberculatus.We first preprocessed and enhanced the collected images of P.tritubereulatus so as to build a Portunus gender classification dataset(PGCD).Besides,we proposed a multi-group convolutional neural network for gender classification of P.tritubereulatus,mainly using ResNet50 to extract features from image patches,thereby reducing information loss during the feature extraction process.In order to focus more on finding useful information of input data,we also constructed an attention mechanism before gender classification to emphasize the importance of details in the global feature map.The results show that the classification accuracy,recall and accuracy of this method on PGCD were 95.59%,94.41%and 96.68%,respectively,with a recognition error rate of only 4.41%.It is concluded that the method has superior classification performance and can be used in automatic classification and recognition systems for Portunus gender.