Lightweight Architecture Optimization for Car Logo Detection Based on Visual State Space Models

Car logo detection is a challenging fine-grained perception task characterized by small object scales and high inter-class similarity. In such scenarios, discriminative cues mainly reside in subtle local textures rather than global structures. Although State Space Model (SSM)-based detectors, such as Mamba-YOLO with its ODMamba backbone, provide efficient long-range dependency modeling, they often struggle to maintain spatial alignment and semantic consistency during multi-scale feature fusion. This limitation reduces their effectiveness in fine-grained discrimination. To address these issues, this paper proposes LVSS-Net (Lightweight Visual State Space Network), a unified framework with three key components. First, Multi-Scale Semantic Alignment (MSAA) preserves spatial resolution while enhancing semantic consistency across feature pyramid levels through adaptive normalization and attention-based fusion. Second, Dynamic Content-Aware Reconstruction (DCR) introduces learnable content-adaptive offsets to model variable sampling positions during feature upsampling, mitigating scale-induced information loss and boundary blurring. Third, Global–Local Saliency Modulation (GLSM) combines parameter-free global saliency modeling with local texture enhancement via a lightweight LTE module to capture fine-grained discriminative patterns. Extensive experiments on the VLD-11 fine-grained car logo dataset and the TT100K traffic sign detection dataset demonstrate that LVSS-Net achieves 98.3% mAP@0.5 with only 1.5 M parameters. The proposed method reduces model complexity by 73.5% compared to the Mamba-YOLO baseline while maintaining competitive detection performance. These results demonstrate its effectiveness in resource-constrained fine-grained detection scenarios.