MFAE-YOLO: Multifeature Attention-Enhanced Network for Remote Sensing Images Object Detection

Object detection in aerial remote sensing images is essential for applications such as traffic management, public security, and ecological monitoring. However, existing methods struggle to handle multi-scale objects, complex backgrounds, and frequent occlusions, resulting in inadequate global feature extraction and unstable multi-scale loss calculations. To address these challenges, we propose the Multi-Feature Attention-Enhanced YOLO (MFAE-YOLO) network. Our approach introduces a Global Feature Fusion Processing (GFFP) module to enhance global feature extraction. The backbone network incorporates Fusion of Channel, Pixel, and Spatial (FCPS) attention modules to strengthen feature representation, while C2F-Feature Pool Extraction Units (C2F-FPEU) optimize pooling-based feature extraction. Additionally, we propose an Accurate IoU (AIoU) loss function to refine bounding box regression. Experiments on NWPU VHR-10, RSOD, and DIOR datasets demonstrate that MFAE-YOLO surpasses state-of-the-art methods, achieving mAP<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> values of 94.7%, 94.8%, and 67.0%, respectively. Ablation studies further validate the contributions of each module. The code is available at https://github.com/yiboCode/MFAE-YOLO.