LVMSOD: Lightweight Visual Mamba Small Object Detection for Autonomous Vehicles

The application of object detection in industrial transportation has witnessed substantial advancements, yielding significant enhancements in both safety and efficiency. While Transformer-based detectors have demonstrated remarkable success in object detection for autonomous driving, their quadratic computational complexity and limited small-object perception capabilities remain significant challenges. To address these limitations, we propose lightweight visual Mamba small object detection (LVMSOD) method, a novel selective state space model designed for small object detection task. The proposed framework employs a multi-level cascade of dual-layer nested Mamba modules to comprehensively capture contextual information across different scales, coupled with a hierarchical feature fusion strategy to enhance multi-scale feature integration for improved small object detection. The LVMSOD framework incorporates two key components to enhance feature representation: Visual Mamba Detection (VDM) block that preserves fine-grained image details, and Lightweight Gated Multi-Layer Perceptron (LGMLP) designed to model local feature dependencies efficiently. Furthermore, we propose an optimized feature extraction mechanism employing depthwise and pointwise convolutions with distribution factors, significantly reducing computational overhead while maintaining detection accuracy. Comprehensive evaluations on benchmark datasets demonstrate the effectiveness of our approach, achieving mAP@0.5 scores of 93.3% on KITTI and 45.3% on VisDrone while maintaining superior computational efficiency. These results highlight the potential of LVMSOD for efficient and accurate small object detection.