CVNet: Lightweight Cross-View Vehicle ReID with Multi-Scale Localization
Cross-view vehicle re-identification (ReID) between aerial and ground perspectives is challenging due to limited computational resources on edge devices and significant scale variations. We propose CVNet, a lightweight network with two key modules: the multi-scale localization (MSL) module and the d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2809 |
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| author | Wenji Yin Baixuan Han Yueping Peng Hexiang Hao Zecong Ye Yu Shen Yanjun Cai Wenchao Kang |
| author_facet | Wenji Yin Baixuan Han Yueping Peng Hexiang Hao Zecong Ye Yu Shen Yanjun Cai Wenchao Kang |
| author_sort | Wenji Yin |
| collection | DOAJ |
| description | Cross-view vehicle re-identification (ReID) between aerial and ground perspectives is challenging due to limited computational resources on edge devices and significant scale variations. We propose CVNet, a lightweight network with two key modules: the multi-scale localization (MSL) module and the deep–shallow filtrate collaboration (DFC) module. The MSL module employs multi-scale depthwise separable convolutions and a localization attention mechanism to extract multi-scale features and localize salient regions, addressing viewpoint variations. DFC employs a dual-branch design comprising deep and shallow branches, integrating a filtration module optimized via neural architecture search, a collaboration module, and lightweight convolutions. This design effectively captures both unique and shared cross-view features, ensuring efficient and robust feature representation. We also release a new CVPair v1.0 dataset, the first benchmark for cross-view ReID, containing 14,969 images of 894 vehicle identities, offering results of traditional and lightweight methods. CVNet achieves state-of-the-art performance on CVPair v1.0, VehicleID, and VeRi776, advancing cross-view vehicle ReID. The dataset will be released publicly. |
| format | Article |
| id | doaj-art-ab081ea696cd46588dda20bead7df37f |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ab081ea696cd46588dda20bead7df37f2025-08-20T02:31:20ZengMDPI AGSensors1424-82202025-04-01259280910.3390/s25092809CVNet: Lightweight Cross-View Vehicle ReID with Multi-Scale LocalizationWenji Yin0Baixuan Han1Yueping Peng2Hexiang Hao3Zecong Ye4Yu Shen5Yanjun Cai6Wenchao Kang7School of Information Engineering, PAP Engineering University, Xi’an 710086, ChinaSchool of Information Engineering, PAP Engineering University, Xi’an 710086, ChinaSchool of Information Engineering, PAP Engineering University, Xi’an 710086, ChinaSchool of Information Engineering, PAP Engineering University, Xi’an 710086, ChinaSchool of Information Engineering, PAP Engineering University, Xi’an 710086, ChinaSchool of Information Engineering, PAP Engineering University, Xi’an 710086, ChinaSchool of Information Engineering, PAP Engineering University, Xi’an 710086, ChinaSchool of Information Engineering, PAP Engineering University, Xi’an 710086, ChinaCross-view vehicle re-identification (ReID) between aerial and ground perspectives is challenging due to limited computational resources on edge devices and significant scale variations. We propose CVNet, a lightweight network with two key modules: the multi-scale localization (MSL) module and the deep–shallow filtrate collaboration (DFC) module. The MSL module employs multi-scale depthwise separable convolutions and a localization attention mechanism to extract multi-scale features and localize salient regions, addressing viewpoint variations. DFC employs a dual-branch design comprising deep and shallow branches, integrating a filtration module optimized via neural architecture search, a collaboration module, and lightweight convolutions. This design effectively captures both unique and shared cross-view features, ensuring efficient and robust feature representation. We also release a new CVPair v1.0 dataset, the first benchmark for cross-view ReID, containing 14,969 images of 894 vehicle identities, offering results of traditional and lightweight methods. CVNet achieves state-of-the-art performance on CVPair v1.0, VehicleID, and VeRi776, advancing cross-view vehicle ReID. The dataset will be released publicly.https://www.mdpi.com/1424-8220/25/9/2809re-identificationcross-viewlightweight network |
| spellingShingle | Wenji Yin Baixuan Han Yueping Peng Hexiang Hao Zecong Ye Yu Shen Yanjun Cai Wenchao Kang CVNet: Lightweight Cross-View Vehicle ReID with Multi-Scale Localization Sensors re-identification cross-view lightweight network |
| title | CVNet: Lightweight Cross-View Vehicle ReID with Multi-Scale Localization |
| title_full | CVNet: Lightweight Cross-View Vehicle ReID with Multi-Scale Localization |
| title_fullStr | CVNet: Lightweight Cross-View Vehicle ReID with Multi-Scale Localization |
| title_full_unstemmed | CVNet: Lightweight Cross-View Vehicle ReID with Multi-Scale Localization |
| title_short | CVNet: Lightweight Cross-View Vehicle ReID with Multi-Scale Localization |
| title_sort | cvnet lightweight cross view vehicle reid with multi scale localization |
| topic | re-identification cross-view lightweight network |
| url | https://www.mdpi.com/1424-8220/25/9/2809 |
| work_keys_str_mv | AT wenjiyin cvnetlightweightcrossviewvehiclereidwithmultiscalelocalization AT baixuanhan cvnetlightweightcrossviewvehiclereidwithmultiscalelocalization AT yuepingpeng cvnetlightweightcrossviewvehiclereidwithmultiscalelocalization AT hexianghao cvnetlightweightcrossviewvehiclereidwithmultiscalelocalization AT zecongye cvnetlightweightcrossviewvehiclereidwithmultiscalelocalization AT yushen cvnetlightweightcrossviewvehiclereidwithmultiscalelocalization AT yanjuncai cvnetlightweightcrossviewvehiclereidwithmultiscalelocalization AT wenchaokang cvnetlightweightcrossviewvehiclereidwithmultiscalelocalization |