MFENet: Multiinformation Feature Enhancement Network for Vehicle Reidentification

Vehicle re-identification (Re-ID) targets cross-camera image retrieval and is a widely used technology in intelligent transportation systems. Current Re-ID methods primarily enhance feature extraction by focusing on either global or local features, but they often fail to effectively leverage diverse information. To address these limitations, we propose a multi-information feature enhancement network (MFENet) that integrates diverse information types to enhance feature representation and boost model accuracy. Specifically, (1) a coarse-grained feature enhancement (CFE) module is employed to remove background influence on image features. This module filters the background, enabling the network model to extract more accurate vehicle features, such as color and model. (2) A fine-grained feature enhancement (FFE) module collects detailed information about vehicles by extracting features from subtle areas (e.g., vehicle lights and rearview mirrors) of an image, providing more unique clues about the vehicle. (3) A latent feature enhancement (LFE) module is designed to mine latent features and enrich vehicle features using non-visual cues, such as the vehicle’s camera and orientation, without relying on image information. Extensive experiments on vehicle Re-ID datasets demonstrate that MFENet outperforms most existing methods.