多尺度卷积神经网络融合Transformer的竹材缺陷识别方法  

Bamboo defect recognition method based on multi-scale convolutional neural network fusion Transformer

作　　者：杨松[1] 张锐[1] 朱良宽[1] YANG Song;ZHANG Rui;ZHU Liangkuan(College of Computer and Control Engineering,Northeast Forestry University,Harbin 150040,China)

机构地区：[1]东北林业大学计算机与控制工程学院,哈尔滨150040

出　　处：《林业工程学报》2024年第5期126-133,共8页Journal of Forestry Engineering

基　　金：黑龙江博士后出站启动金;中央高校基本科研业务费专项资金(DL12BB10-11)。

摘　　要：在竹材缺陷识别的研究中,竹片形状、缺陷部位颜色深浅及裂纹大小差异都是制约模型识别准确率的关键。针对上述问题,提出一种适用于中小数据集的多尺度卷积神经网络融合Transformer的竹材缺陷识别方法,以更好地提高竹材缺陷识别的准确率。该方法在卷积神经网络的主干上进行改进,从获取不同尺度语义信息的角度出发,首先利用卷积神经网络在不同尺度的特征图上捕捉图像局部语义信息,然后将不同尺度的语义特征映射为特征符号,同时引入Sinkhorn分词器对不同阶段的卷积神经网络特征符号化以减少特征冗余,再通过Transformer对特征符号之间的关系进行建模以学习图像全局语义信息。试验结果表明,与VGG16、ResNet50、DenseNet121、ViT这4种深度学习模型相比,基于多尺度卷积神经网络融合Transformer的方法能够更高效地提高竹材缺陷识别模型的性能,在竹材缺陷图像数据集上的平均识别准确率达到了99.13%。该方法识别速度更快、精度更高,且具有良好的鲁棒性,为竹材缺陷的实时自动识别提供了新思路,同时也验证了所提出方法的有效性。In the research of bamboo defect recognition,the different shapes of bamboo slices,the different color shades of defect areas,and the size of cracks are key issues that influence the accuracy of model recognition.A multi-scale convolutional neural network fused with Transformer architecture was proposed for bamboo defect recognition,particularly suitable for small and medium-sized datasets,aiming at enhancing the accuracy of defect detection.This study improved the backbone of convolutional neural networks,by focusing on capturing semantic information at various scales.Firstly,convolutional neural networks were used to capture local semantic information of images on feature maps of different scales.Then,semantic features of different scales were mapped into feature symbols.At the same time,a Sinkhorn tokenizer was introduced to symbolize the features of convolutional neural networks at different stages to reduce feature redundancy.Finally,Transformer was used to model the relationship between feature symbols to learn global semantic information of images.A series of experiments were conducted to evaluate the performance of the proposed method.This study utilized a dataset comprising 6360 images of bamboo defects categorized into four types.The dataset was split at a ratio of 8∶2 for training and validation.During the training phase,an end-to-end training strategy was employed,optimizing model hyperparameters by minimizing the loss function.In the inference phase,the model was evaluated using the set of validation,and performance differences were compared between the proposed method and deep learning models including VGG16,ResNet50,DenseNet121 and ViT.The experimental results showed that the method based on multi-scale convolutional neural network fusion Transformer can more efficiently improve the performance of the bamboo defect recognition model compared with the four deep learning models,i.e.,VGG16,ResNet50,DenseNet121 and ViT.The average recognition accuracy on the bamboo defect image dataset in this study

关 键 词：竹材缺陷识别 多尺度 卷积神经网络 TRANSFORMER Sinkhorn分词器 

分 类 号：S762[农业科学—森林保护学] TP242[农业科学—林学]