M2FE-YOLO: Multi-Branch and Multi-Level Feature Enhancement Network for Remote Sensing Object Detection

The detection of remote sensing (RS) objects plays a crucial role in various earth observation tasks. Current RS object detection methods tend to face great challenges due to large-scale variations of object sizes and limited representation of semantic features, which affect the final bounding box regression results in practice. To address the challenges, we propose a novel Multi-branch and Multi-level Feature Enhancement framework (M2FE-YOLO) to refine feature representation learning for improving the performance of object detection. The proposed M2FE-YOLO primarily comprises three components, i.e., Multi-Branch Feature-awareness based CSP (MBFA-CSP) module, Multi-Level Feature Fusion (MLFF) module, and Shape-IoU as a loss function. MBFA-CSP builds on a dual-scale feature-aware mechanism and a serial convolution pathway to dynamically adjust receptive fields, capturing critical contextual patterns in RS images. MLFF resolves the persistent semantic discrepancy between low-level texture features and high-level abstract representations, enabling precise localization of objects in cluttered RS landscapes. Shape-IoU instead of C-IoU incorporates a geometric compatibility factor (i.e., aspect ratio consistency), and is more crucial for bounding box regression of elongated or irregular RS objects. Compared with existing state-of-the-art methods, extensive experiments on RSOD, NWPU VHR-10, and DOTA datasets quantitatively and qualitatively demonstrate the superiority of the proposed M2FE-YOLO method, achieving up to 92.1%, 93.3%, and 72.2% mAP, respectively. Meanwhile, M2FE-YOLO-OBB achieves an excellent detection result of 73.9% mAP on DOTA dataset for oriented object detection task.