CMNet: Global–Local Feature Fusion CNN-Mamba Network for Remote Sensing Object Detection

In the field of remote sensing object detection (RSOD), significant challenges remain, including the vast field of view in remote sensing images, the diverse array of target categories, and complex backgrounds. Traditional methods for processing remote sensing images face limitations in this context. While convolutional neural networks (CNNs) can expand the receptive field by utilizing kernels of different sizes, larger kernels increase the number of parameters and introduce noise. Vision Transformers (ViT) achieve global receptive fields through their global attention mechanism. However, their quadratic computational complexity struggles with high-resolution images. Recently, Mamba has gained prominence in image processing. Its unique four-directional scanning mechanism allows focusing on regions of interest from multiple angles while maintaining linear model complexity and achieving global receptive fields. In this work, we propose a new CNN–Mamba network (CMNet) that synergistically exploits the advantages of both architectures. Specifically, we employ VMamba(VM) to extract global semantic features from images. Moreover, we design a multi-scale local feature extraction (MLFE) module, which captures local texture information and edge details through the local feature extraction (LFE) and the global attention module (GAM). The synergy between VMamba and MLFE creates complementary global–local features. To address the representational differences between these two kinds of features, we further design a feature cross-complementary (FCC) module. This module achieves cross-complementarity of features, solving feature disparity issues. Our CMNet achieves 79.38% mAP50 on the DOTA v1.0 dataset and 90.60% mAP50 on the HRSC dataset, outperforming existing state-of-the-art approaches.