【摘要】 水果作为人们日常必备的食物，其越来越高的消费需求使得行业对自动损伤检测和自动分类的要求日益提高。针对这一需求，近年来水果损伤自动检测成为研究的热门话题。针对现有的深度学习技术，即卷积神经网络在水果的特征提取和分类方面的应用进行了探讨，提出了一种以ResNet34作为主干网络，并在此基础上引入注意力机制SE和CBAM模块的方法来实现水果损伤的检测和基本分类。在fruit fresh and rotten for classification数据集上完成了该方法的验证。经过与VGG16,GoogLeNet,MobileNetV2等常见网络的比较分析，结果显示改进后的模型分类准确度达到98.8%。通过加入新的苹果数据集，该模型相比原网络ResNet34，在性能方面进一步提升，有效提高了模型的泛化性。该模型提升了水果损伤检测和分类处理的精确性，在实际中，可为复杂的水果图片的多特征分类处理提供借鉴。更多还原

【Abstract】 For the daily essential food of people, automatic damage detection and automatic classification are essential for the increasing consumption of fruit. In view of this demand,automatic detection of fruit damage has become a hot topic in recent years. In this paper, the application of convolutional neural network, an existing deep learning technology, in fruit feature extraction and classification was discussed. A method based on ResNet34 as the backbone network and the introduction of attention mechanism SE and CBAM module was proposed to realize the detection and basic classification of fruit damage. The method was verified on fruit fresh and rotten for classification data set, and compared with VGG16, GoogLeNet, MobileNetV2 and other common networks. The accuracy of fruit damage detection and classification is improved. The classification accuracy reaches 98.8%. By adding the new apple data set, the performance of the model is further improved, compared with the original network ResNet34, and the generalization of the model is effectively improved, which provides a reference for the complex multi-feature classification of actual fruit images.更多还原