MV-YOLO: An Efficient Small Object Detection Framework Based on Mamba
Shixiao Wu, Xingyuan Lu, Chengcheng Guo, Hong Yu Guo · IEEE Transactions on Geoscience and Remote Sensing · 2025
Small-object detection in UAV and remote-sensing imagery remains challenging because targets occupy only a few pixels, features are sparse, objects are often occluded, and backgrounds are complex. To tackle these issues, we propose MV-YOLO, a lightweight, one-stage detector built from three complementary modules. First, the Mamba Vision Module (MVM) leverages a state-space model to capture global contextual dependencies across the feature map with linear complexity, while its variant (MSAVM) further incorporates multi-head self-attention to enhance long-range interactions. Second, the Bio-inspired Hierarchical Feature Modulation (BHFM) module decomposes large convolutional kernels into asymmetric and dilated convolutions—expanding the receptive field to 19×19—alongside parallel spatial and channel attention branches to retain high-frequency details crucial for small objects. Third, the Context Clue Guided Module (CCGM) learns sampling offsets to dynamically upsample low-resolution feature maps and fuses them with high-resolution features, sharpening small-object representations. Extensive experiments on VisDrone2019, DIOR, and UCAS-AOD achieve mAP50 scores of 50.6%, 89.7%, and 97.6%, respectively, demonstrating MV-YOLO’s efficiency and effectiveness for real-world aerial and remote-sensing applications.