MFRA-YOLOv11: Remote Sensing Small Object Detection Algorithm Based on Multiscale Feature Extraction and Region Awareness

Abstract

Small objects in remote sensing images often exhibit blurred edges and dense distributions. This makes it difficult to precisely localize object regions. These challenges are especially pronounced on devices with limited computational capacity, where accuracy and efficiency are both critical. To address these challenges, we propose MFRA-YOLOv11, an enhanced YOLOv11s-based network for remote sensing small object detection under the horizontal bounding box paradigm, which integrates multiscale feature extraction and object region awareness to improve detection accuracy. First, in the backbone, we introduce the CSP bottleneck with triple attention aggregation module to emphasize object regions. This module combines channel, coordinate, and kernel attention to aggregate features, enhancing the localization and representation of small objects. Second, a multiscale feature extraction module is integrated into the neck to enhance feature representation across different scales capturing multiscale features along horizontal and vertical directions under varied receptive fields, further boosting small object detection. Finally, we incorporate an adaptive multi-receptive field module into the detection head, which adaptively selects appropriate receptive fields for feature maps of varying granularity, aiding the head in accurate object localization. We validated the accuracy of MFRA-YOLOv11 on the NWPU VHR-10, VEDAI, and DOTA datasets. Compared to YOLOv11, our model achieves 3.0%, 2.7%, and 3.6% improvements in mAP50 on these three datasets, respectively, and 2.4%, 3.5%, and 4.0% improvements in mAP50–95, with only a slight increase in computational cost (15.9% in parameters and 10.2% in GFLOPs).