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    王佳盛, 陈燕, 曾泽钦, 等. 基于全卷积神经网络的荔枝表皮缺陷提取[J]. 华南农业大学学报, 2018, 39(6): 104-110. doi: 10.7671/j.issn.1001-411X.2018.06.016
    引用本文: 王佳盛, 陈燕, 曾泽钦, 等. 基于全卷积神经网络的荔枝表皮缺陷提取[J]. 华南农业大学学报, 2018, 39(6): 104-110. doi: 10.7671/j.issn.1001-411X.2018.06.016
    shu
    WANG Jiasheng, CHEN Yan, ZENG Zeqin, LI Jiawei, LIU Weiwei, ZOU Xiangjun. Extraction of litchi fruit pericarp defect based on a fully convolutional neural network[J]. Journal of South China Agricultural University, 2018, 39(6): 104-110. DOI: 10.7671/j.issn.1001-411X.2018.06.016
    Citation: WANG Jiasheng, CHEN Yan, ZENG Zeqin, LI Jiawei, LIU Weiwei, ZOU Xiangjun. Extraction of litchi fruit pericarp defect based on a fully convolutional neural network[J]. Journal of South China Agricultural University, 2018, 39(6): 104-110. DOI: 10.7671/j.issn.1001-411X.2018.06.016
    shu

    基于全卷积神经网络的荔枝表皮缺陷提取

    Extraction of litchi fruit pericarp defect based on a fully convolutional neural network

      摘要
    • 摘要:
      目的  增强荔枝表皮缺陷提取效果，满足其品质检测分级准确性要求。
      方法  采用Tensorflow框架构建基于AlexNet的全卷积神经网络AlexNet-FCN，以ReLU为激活函数，Max-pooling为下采样方法，Softmax回归分类器的损失函数作为优化目标，建立荔枝表皮缺陷提取的全卷积神经网络模型，并用批量随机梯度下降法对模型进行优化。
      结果  模型收敛后在验证集上裂果交并比 (IoUd) 为0.83，褐变交并比 (IoUb) 为0.60，褐变与裂果的总体交并比 (IoUa) 为0.68；与利用线性SVM、朴素贝叶斯分类器缺陷提取效果相比，该模型的特征提取能力显著提高。
      结论  全卷积神经网络在水果表面缺陷提取中具有良好的应用前景。

       

      Abstract:
      Objective  To enhance the effects of litchi fruit pericarp defect extraction and satisfy the accuracy requirements of quality detection and classification.
      Method  A fully convolutional neural network was built up based on AlexNet (AlexNet-FCN) using Tensorflow framework, with ReLU as the activation function, Max-pooling as the down-sampling method and loss function of Softmax regression classifier as the optimization target. Mini-batch stochastic gradient descent (Mini-batch SGD) was used to optimize the model.
      Result  When the model was converged, the intersection-over-union of dehiscent area (IoUd) of litchi fruit cracking was 0.83 for the validation set, the intersection-over-union of brown area (IoUb) was 0.60, and the intersection-over-union of both dehiscent and brown area (IoUa) was 0.68. Compared with linear-support vector machine (SVM) and Naïve Bayes classifier, AlexNet-FCN had a stronger defect extraction ability.
      Conclusion  Fully convolutional networks (FCN) have a good prospect for application of fruit pericarp defect extraction.

       

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