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    刘小刚, 李荣梅, 范诚, 等. 基于语义分割的芒果表皮缺陷识别[J]. 华南农业大学学报, 2023, 44(1): 134-141. DOI: 10.7671/j.issn.1001-411X.202204014
    引用本文: 刘小刚, 李荣梅, 范诚, 等. 基于语义分割的芒果表皮缺陷识别[J]. 华南农业大学学报, 2023, 44(1): 134-141. DOI: 10.7671/j.issn.1001-411X.202204014
    shu
    LIU Xiaogang, LI Rongmei, FAN Cheng, et al. Recognition of mango skin defect based on semantic segmentation[J]. Journal of South China Agricultural University, 2023, 44(1): 134-141. DOI: 10.7671/j.issn.1001-411X.202204014
    Citation: LIU Xiaogang, LI Rongmei, FAN Cheng, et al. Recognition of mango skin defect based on semantic segmentation[J]. Journal of South China Agricultural University, 2023, 44(1): 134-141. DOI: 10.7671/j.issn.1001-411X.202204014
    shu

    基于语义分割的芒果表皮缺陷识别

    Recognition of mango skin defect based on semantic segmentation

      摘要
    • 摘要:
      目的  运用语义分割技术自动识别芒果及其表皮缺陷，实现芒果的质量评估及分选，为芒果质量快速无损检测提供参考。
      方法  采集自然环境下的多场景芒果表皮缺陷图像用于模型的训练与测试，将联合上采样金字塔(Joint pyramid upsampling，JPU)结构替换DeepLabV3+中空洞空间卷积池化金字塔(Atrous spatial pyramid pooling，ASPP)，将Atrous-ResNet模型替换DeepLabV3+中Xception模型，采用类别像素准确率(Class pixel accuracy，CPA)、平均像素准确率(Mean pixel accuracy，MPA)、平均交并比 (Mean intersection over union，MIoU)作为模型的精度评价指标。
      结果  采用JPU模块替换ASPP模块，在ResNet网络中运用扩张卷积有利于增大模型的感受野，总体上预测的边界更加平滑，且对细小缺陷的识别更精确；与SegNet、LinkNet算法的对比验证表明，Atrous-ResNet模型具备更高的精度，CPA小幅提升，MPA提升3.79个百分点，MIoU提升4.57个百分点，Atrous-ResNet模型具有更好的识别效果。
      结论  基于语义分割的方法应用于芒果表皮缺陷识别是可行的，Atrous-ResNet模型较SegNet以及LinkNet算法比较具有更高的识别精度。

       

      Abstract:
      Objective  The semantic segmentation technology was used to automatically identify mango and its skin defects, to realize the quality evaluation and sorting of mango and provide a reference for the rapid and nondestructive testing of mango quality.
      Method  Mango skin defect images in multi-scene of natural environment were collected for model training and testing. Atrous spatial pyramid pooling (ASPP) in DeepLabV3+ was replaced by joint pyramid upsampling (JPU) structure, and Xception model in DeepLabV3+ was replaced by Atrous-ResNet model. Class pixel accuracy (CPA), mean pixel accuracy (MPA) and mean intersection over union (MIoU) were used as the accuracy evaluation indexes of each model.
      Result  JPU module was used to replace ASPP module, and Atrous convolution was applied to ResNet network which was conductive to increase the receptive field of the model. In general, the predicted boundary was smoother, and the identification of small defects was more accurate. The comparison with SegNet and LinkNet algorithms showed that Atrous-ResNet model had higher accuracy, with CPA slightly improved, MPA was up 3.79 percent point and MIoU was up 4.57 percent. Atrous-ResNet model had better identification effects.
      Conclusion  The method based on semantic segmentation is feasible for mango skin defect recognition. Compared with SegNet and LinkNet algorithms, Atrous-ResNet model has higher recognition accuracy.

       

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