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基于多组卷积神经网络的梭子蟹性别识别研究

魏天琪,  郑雄胜,  李天兵,  王日成

文章导航 > 南方水产科学  > 2023 > 优先出版
魏天琪, 郑雄胜, 李天兵, 王日成. 基于多组卷积神经网络的梭子蟹性别识别研究[J]. 南方水产科学. doi: 10.12131/20230107
引用本文: 魏天琪, 郑雄胜, 李天兵, 王日成. 基于多组卷积神经网络的梭子蟹性别识别研究[J]. 南方水产科学. doi: 10.12131/20230107
shu
WEI Tianqi, ZHENG Xiongsheng, LI Tianbing, WANG Richeng. Multi-group convolutional neural network for gender recognition of Portunus tritubereulatus[J]. South China Fisheries Science. doi: 10.12131/20230107
Citation: WEI Tianqi, ZHENG Xiongsheng, LI Tianbing, WANG Richeng. Multi-group convolutional neural network for gender recognition of Portunus tritubereulatus[J]. South China Fisheries Science. doi: 10.12131/20230107
shu

基于多组卷积神经网络的梭子蟹性别识别研究

doi: 10.12131/20230107
  • 1.

    浙江海洋大学 海洋工程装备学院，浙江 舟山 316002

  • 2.

    合肥城市学院，安徽 合肥 231131

基金项目: 浙江省科技厅尖兵领雁计划项目 (2022C02001) ；舟山市科技计划项目 (2021C21005)
详细信息
    作者简介:

    魏天琪 (1994—)，女，硕士研究生，研究方向为梭子蟹性别识别、分类等。E-mail: 18895685076@163.com

    通讯作者:

    郑雄胜  (1972—)，男，副教授，硕士，研究方向为涉海机械装备设计与仿真分析等。E-mail: zxs668998@163.com

  • 中图分类号: TP 391.4

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

  • 1.

    School of Marine Engineering Equipment, Zhejiang Ocean University, Zhoushan 316002, China

  • 2.

    City University of Hefei, Hefei 231131, China

    摘要
  • 摘要: 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解决的关键技术。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。
    Abstract: 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 the 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.

  • 图  1  所提方法的总体架构

    Figure  1.  Overall architecture of our approach

    下载: 全尺寸图片 幻灯片

    图  2  部分梭子蟹样本 (左：雌性；右：雄性) 

    Figure  2.  Samples of Portunid (Left: female; Right: male) 

    下载: 全尺寸图片 幻灯片

    图  3  两种降低像素的效果对比

    Figure  3.  Comparison of two pixel reduction effects

    下载: 全尺寸图片 幻灯片

    图  4  5种不同类型的数据增强技术的示例

    Figure  4.  Examples of five different types of data enhancement technologies

    下载: 全尺寸图片 幻灯片

    图  5  深度提取特征模块

    Figure  5.  Depth extraction feature module

    下载: 全尺寸图片 幻灯片

    图  6  ResNet50[12]残差模块示意图

    Figure  6.  Residuals block diagram of ResNet50[12]

    下载: 全尺寸图片 幻灯片

    图  7  融合特征分类模块

    Figure  7.  Fusion feature classification module

    下载: 全尺寸图片 幻灯片

    图  8  混淆矩阵

    注：TP. 真阳性；FN. 假阴性；FP. 假阳性；TN. 真阴性。

    Figure  8.  Confusion matrix

    Note: TP.True positive; FN. False negative; FP. False positive; TN.True negative.

    下载: 全尺寸图片 幻灯片

    图  9  梭子蟹性别分类的混淆矩阵

    Figure  9.  Confusion matrix of gender classification of P. tritubereulatus

    下载: 全尺寸图片 幻灯片

    图  10  比较不同网络性能的ROC曲线和AUC

    Figure  10.  ROC curve and AUC to compare performance of different networks

    下载: 全尺寸图片 幻灯片

    图  11  单幅图像预测概率

    Figure  11.  Prediction probability of single image

    下载: 全尺寸图片 幻灯片

    表  1  不同骨干模型对MGCNN性能的影响

    Table  1.   Effects of different backbone models on MGCNN performance

    骨干模型
    Backbone model    		准确率 
    Accuracy/% 
    VGG VGG11 76.88
    VGG13 82.59
    VGG16 89.76
    VGG19 86.09
    ResNet ResNet18 90.44
    ResNet34 91.15
    ResNet50 92.79
    ResNet101 92.21
    ResNet152 88.24
    下载: 导出CSV

    表  2  不同优化器对MGCNN性能的影响

    Table  2.   Effects of different optimizers on MGCNN performance

    骨干模型
    Backbone model    		优化器
    Optimizer    		准确率 
    Accuracy/% 
    ResNet50 SGD 92.79
    AdaGrad 89.56
    RMSprop 95.15
    Adam 95.29
    Adamax 93.82
    ASGD 92.65
    下载: 导出CSV

    表  3  不同参数对MGCNN性能的影响

    Table  3.   Effects of different parameters on MGCNN performance

    学习率
    Learning Rate    		批大小
    Batch Size    		准确率
    Accuracy/% 
    0.000 1 32 92.94
    0.000 5 95.00
    0.001 0 95.29
    0.001 5 95.59
    0.002 0 92.65
    0.001 5 64 94.56
    32 95.59
    16 95.15
    下载: 导出CSV

    表  4  MGCNN与先进方法的比较

    Table  4.   Comparison between MGCNN and state-of-the-art methods

    方法
    Method    		准确率
    Accuracy/%     		召回率
    Recall/%     		查准率
    Precision/%     		错误率
    Error/% 
    AlexNet54.7159.3667.9345.29
    VGG1689.8588.2491.1910.15
    ResNet15294.5694.7194.435.44
    DenseNet12194.4194.4194.415.59
    InceptionV395.0092.6597.225.00
    MGCNN
     (本研究方法 Our method)     		95.5994.4196.684.41
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-20
  • 修回日期:  2023-07-31
  • 录用日期:  2023-09-09
  • 网络出版日期:  2023-09-13

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