【摘要】 目的:解决目前红枣检测过程中果梗/花萼容易被误识别为缺陷枣的问题。方法:提出一种基于深度学习和图像处理的干制哈密大枣果梗/花萼及缺陷识别方法。通过改进深度残差网络ResNeXt-50,采用感兴趣区域提取方法和迁移学习技术提出一种TL-ROI-X-ResNext-50分类模型,实现干制哈密大枣果梗/花萼及缺陷分类。结果:通过模型试验对比,感兴趣区域提取方法和迁移学习技术可以减少模型计算成本,提高准确率,模型识别准确率可达94.17%。结论:该方法可初步满足干制哈密大枣果梗/花萼及缺陷在线检测装备的生产需求。更多还原

【Abstract】 Objective: This study focuses on solving the problems that stem and calyx are mistakenly recognized as defective jujube in the current detection process of jujube. Methods: A method for identifying stem/calyx and defects of dried Hami jujube based on deep learning and image processing was proposed. By improving the depth residual network ResNeXt-50, using the region of interest extraction method and transfer learning technology, TL-ROI-X-ResNeXt-50 classification model was proposed to realize the classification of stem/calyx and defects of dried Hami jujube. Results: Through the comparison of model experiments, the region of interest extraction method and transfer learning technology could reduce the calculation cost of the model and improve the accuracy. The accuracy of model recognition could reached 94.17%. Conclusion: The method can initially meet the production requirements of on-line detection equipment for stem/calyx and defects of dried Hami jujube, and provide theoretical basis and technical reference for the development of rapid nondestructive detection system for stem/calyx and defects of other similar fruits.更多还原