SAMYOLO: Integrating Super-Resolution and Vision Mamba for Enhanced Small Object Detection

Small object detection remains a significant challenge in computer vision, primarily attributed to insufficient feature information and low resolution. This paper introduces SAMYOLO, a novel framework that integrates super-resolution (SR) technology with a mamba-based feature enhancement strategy to improve small object detection. SAMYOLO incorporates a dual-branch design to process both RGB and super-resolved images, improving detail preservation and feature representation. A dedicated feature mapping (FM) module, built upon the State Space Model (Mamba), is designed to reinforce critical features and enable effective cross-modal fusion at multiple scales. Furthermore, an optimized loss function is introduced to enhance detection accuracy and training stability. Experimental results on the SIMD and VEDAI datasets demonstrate SAMYOLO's superior performance, achieving significant improvements in mAP50 over state-of-the-art models. The framework excels in dense small object scenarios, highlighting its practical potential for applications such as remote sensing and aerial imagery analysis.