【摘要】 [目的]利用现代计算机视觉技术和深度学习方法，提升腐烂草莓分类的准确性和效率。[方法]提出了基于EfficientNet V2融合GCN和CA-Transformer的腐烂草莓分类方法。为基准模型添加了图卷积分支，通过聚合节点的周围信息来更新特征表示，更好地捕捉节点在图结构中的上下文信息；将带有注意力的Transformer结构融合到基准模型的主干中，用该结构替换部分卷积操作，实现全局和局部特征的融合，从而更好地识别草莓的腐烂情况；在传统残差结构的基础上引入学习参数，以实现特征的动态融合。[结果]GC-EfficientNet V2模型相比基准模型在准确率上提高了1.86%，召回率提升了1.49%。与Inception V3、ResNet50、VGGNet、Vision Transformer和EfficientNet V2-m模型相比，该模型的识别准确率分别提高了0.93%,2.08%,2.79%,3.26%,0.47%。[结论]该模型能够准确地对腐烂草莓进行分类。更多还原

【Abstract】 [Objective] Improving the accuracy and efficiency of rotting strawberry classification using modern computer vision techniques and deep learning methods. [Methods] A classification method for rotten strawberries based on EfficientNet V2 fusion with Graph Convolutional Network(GCN) and Channel-Attention Transformer(CA-Transformer) has been proposed. Firstly, a graph convolution branch was added to the baseline model, which updated feature representations by aggregating the surrounding information of nodes, better capturing the contextual information of nodes in the graph structure. Secondly, this study integrated the Transformer structure with attention into the backbone of the baseline model, replacing some convolution operations with this structure to achieve the fusion of global and local features, thereby better identifying the rottenness of strawberries. Finally, learning parameters were introduced on the basis of the traditional residual structure to achieve dynamic feature fusion. [Results] The GC-EfficientNet V2 model improved the accuracy by 1.86% and the recall by 1.49% compared to the baseline model. Compared with Inception V3, ResNet50, VGGNet, Vision Transformer, and EfficientNet V2-m, the recognition accuracy of the model was improved by 0.93%, 2.08%, 2.79%, 3.26%, and 0.47%, respectively. [Conclusion] This model can accurately classify rotten strawberries, providing some theoretical support for automatic strawberry sorting.更多还原