文献类型： 外文期刊

作者： Yu, Yang ¹ ; He, Kun ¹ ; Yan, Gang ¹ ; Cen, Shixin ² ; Li, Yang ³ ; Yu, Ming ¹ ;

作者机构： 1.Hebei Univ Technol, Sch Artificial Intelligence, Tianjin 300401, Peoples R China

2.Hebei Univ Technol, Sch Elect & Informat Engn, Tianjin, Peoples R China

3.Tianjin Acad Agr Sci, Inst Informat, Tianjin, Peoples R China

关键词： Vehicle re-identification; deep learning; multi-receptive fields; feature erasure; knowledge distillation

期刊名称：JOURNAL OF INTELLIGENT & FUZZY SYSTEMS （ 影响因子：2.0； 五年影响因子：1.9 ）

ISSN： 1064-1246

年卷期： 2023 年 44 卷 1 期

页码：

收录情况： SCI

摘要： Vehicle Re-Identification (Re-ID) aims to discover and match target vehicles in different cameras of road surveillance. The high similarity between vehicle appearances and the dramatic variations in viewpoints and illumination cause great challenges for vehicle Re-ID. Meanwhile, in safety supervision and intelligent traffic systems, one needs a quick efficient method of identifying target vehicles. In this paper, we propose a Multi-Attention Guided Feature Enhancement Network (MAFEN) to extract robust vehicle appearance features. Specifically, the Fusing Spatial-Channel information multi-receptive fields Feature Enhancement module (FSCFE) is first proposed to aggregate richer and more representative multi-receptive fields features at different receptive fields sizes. It also learned the spatial structure information and channel dependencies of the multi-receptive fields features and embedded them to enhance the feature. Then, we construct the Spatial Attention-Guided Adaptive Feature Erasure (SAAFE) module, which uses spatial attention to erase the most distinguishing features. The networks attention is shifted to potentially salient features to strengthen the ability of the network to extract salient features. In addition, a multi-loss knowledge distillation (MLKD) method using MAFEN as a teacher network is designed to improve computational efficiency. It uses multiple loss functions to jointly supervise the student network. Experimental results on three public datasets demonstrate the merits of the proposed method over the state-of-the-art methods.