计算机工程与应用2024，Vol.60Issue(8)：131-139,9.DOI:10.3778/j.issn.1002-8331.2212-0100
结合数据增强的跨模态行人重识别轻量网络
Cross-Modal Re-Identification Light Weight Network Combined with Data Enhancement
摘要
Abstract
Among the existing cross modal re-identification methods,the research on lightweight network is less.Consid-ering the requirement of hardware deployment for lightweight network,a new cross modal re-identification lightweight network is proposed.Based on Osnet,the feature extractor and feature embedder are split.At the same time,data enhance-ment operations are used to maximize the use of limited data sets to improve network robustness,and the hard triplet loss is improved to further reduce the computation and reduce the difference between modals,so as to improve the accuracy of network identification.The network is lightweight,simple in structure and remarkable in effect.In the all search mode of SYSU-MM01 dataset,the rank-1/mAP of the proposed method reaches 65.56%,61.36%respectively,and the number of parameters is only 1.92×106.关键词
深度可分离卷积/行人重识别/轻量化网络/难样本三元组损失函数Key words
depth separable convolution/person re-identification/lightweight network/hard triplet loss function分类
信息技术与安全科学引用本文复制引用
曹钢钢,王帮海,宋雨..结合数据增强的跨模态行人重识别轻量网络[J].计算机工程与应用,2024,60(8):131-139,9.基金项目
国家自然科学基金面上项目(62072119). （62072119）
