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基于改进AlignedReID++的行人重识别方法
Pedestrian Re-identification Method Based on Improved AlignedReID++
宋建辉;马赫遥;赵亚威; SONG Jianhui;MA Heyao;ZHAO Yawei;由于行人姿态变化的多样性、背景环境的复杂性等因素的干扰，会导致行人特征的表达能力不足，进而影响到行人重识别的准确性。本文基于深度学习的AlignedReID++模型进行改进，将DenseNet121网络与AlignedReID++模型的主干网络ResNet50融合，利用两者的优点，增强模型的特征提取能力；通过引入跨维交互注意力模块和正则化项，限制模型的复杂度，增强表达特征能力，提高泛化性能，进而提升重识别能力。将改进模型在Market1501、DukeMTMC数据集上进行验证，实验结果表明，相较于原始模型，平均精度均值在Market1501数据集上提升了5.6个百分点；在DukeMTMC数据集上提升了5.9个百分点，表明了改进后算法的有效性。
The interference factors such as the diversity of pedestrian pose variations and the complexity of background environments can lead to inadequate capability to express pedestrian features, thereby affecting the accuracy of pedestrian re-identification.This paper aims to improve the AlignedReID++ model based on deep learning by integrating the DenseNet121 network with the backbone ResNet50 network of AlignedReID++.Leveraging the strengths of both networks enhances the model's feature extraction capability.By introducing a cross-dimensional interactive attention module and regularization terms, the model's complexity is constrained, its feature representation capability is strengthened, and its generalization performance is improved, thereby boosting re-identification accuracy.Experimental validation on the Market1501 and DukeMTMC datasets demonstrates that, compared to the original model, the improved model achieves increases by 5.6 percentage points in MAP on the Market1501 dataset, and 5.9 percentage points in MAP on the DukeMTMC dataset.These results confirm the effectiveness of the proposed algorithm.
基于MCA与改进ProtoNet的滚动轴承小样本故障诊断方法
Few-Shot Fault Diagnosis Method for Rolling Bearings Based on MCA and Improved ProtoNet
崔琪;吴东升; CUI Qi;WU Dongsheng;针对工业环境中轴承故障数据稀缺、工况多变且常伴随噪声干扰导致特征提取困难、诊断准确率降低的问题，提出一种基于多维协作注意力(MCA)与改进原型网络(ProtoNet)的滚动轴承小样本故障诊断方法DMCA-ProtoNet。首先，采用离散小波变换(DWT)对轴承二维灰度图数据进行去噪和高频分解；其次，将MCA嵌入残差网络ResNet以提升模型的特征提取能力；最后，对ProtoNet中度量准则进行更新，采用马氏距离代替传统的欧氏距离。实验采用德国帕德博恩大学轴承数据集以及东南大学轴承数据集对模型进行验证，结果表明：在小样本变工况下，模型能够保持较高的诊断准确率，识别率可达到95.94%;在强噪声干扰下本模型具有较好的稳定性与准确率，验证了DMCA-ProtoNet模型在小样本变工况下依然具有良好的泛化能力以及抗噪能力。
To address the issues that bearing fault data is scarce in industrial environment, working conditions are changeable and often interfered by noise, which lead to difficulty in feature extraction and low diagnostic accuracy, a few-shot fault diagnosis method for rolling bearings based on multi-channel attention(MCA)and improved prototype network(ProtoNet)(DMCA-ProtoNet)is proposed.Firstly, the Discrete Wavelet Transform(DWT)is used to denoise the two-dimensional grayscale data of the bearing and decompose the high-frequency of it.Secondly, the multi-dimensional collaborative attention mechanism is embedded into ResNet to improve the feature extraction ability of the model.Finally, the metric criterion in ProtoNet is updated to replace the traditional Euclidean distance with the Marhalanobis distance.The results show that the model can maintain a high diagnostic accuracy under few-shot variable working conditions, and the recognition rate can reach 95.94%.The model has good stability and accuracy under strong noise interference, which verifies that the DMCA-ProtoNet model still has good generalization ability and anti-noise ability under few-shot variable conditions.
公告栏
期刊信息
期刊名称： 沈阳理工大学学报（Journal of Shenyang Ligong University）
创办日期： 1982年
主管单位： 辽宁省教育厅
主办单位： 沈阳理工大学
出版单位：《沈阳理工大学学报》编辑部
刊期： 双月刊
电话： 024-24686097
Email： sgxb6097@sylu.edu.cn
国内统一刊号(CN)： CN 21-1594/T
国际标准刊号(ISSN)：ISSN 1003-1251