结合数据增强的跨模态行人重识别轻量网络

doi:10.3778/j.issn.1002-8331.2212-0100

摘要/Abstract

摘要： 现有的跨模态行人重识别方法中，轻量化网络的相关研究较少。考虑到硬件部署对轻量化网络的需求，提出新的跨模态行人重识别轻量网络。以Osnet为基础，进行特征提取器和特征嵌入器的拆分。同时使用数据增强操作，利用有限的数据集，最大程度提高了网络的鲁棒性。改进难样本三元组损失函数，在减少计算量的同时缩小模态间差异，提升网络识别准确率。提出的轻量化网络结构简单且效果显著，在SYSU-MM01数据集的全搜索模式下rank-1/mAP分别达到65.56%、61.36%，参数量仅为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

Abstract: Among the existing cross modal re-identification methods, the research on lightweight network is less. Considering 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 enhancement 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.

Key words: depth separable convolution, person re-identification, lightweight network, hard triplet loss function

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.

CAO Ganggang, WANG Banghai, SONG Yu. Cross-Modal Re-Identification Light Weight Network Combined with Data Enhancement[J]. Computer Engineering and Applications, 2024, 60(8): 131-139.

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