MFEAD-SODNet: multi-dimensional feature enhancement and multi-scale feature adaptive aggregation and diffusion network for small object detection
Pan Yi, Kun Cao, Chunhua Wei, Lei Liang, Zhisheng Gao · Measurement Science and Technology · 2025
Abstract Small object detection presents various challenges across different domains, with UAV aerial image detection being particularly significant and complex. The detection accuracy is primarily influenced by the high density of small objects, substantial object scale variations and background complexity. Nevertheless, existing object detection algorithms exhibit deficiencies in feature retention and multi-scale feature fusion, thereby limiting detection performance in intricate scenes. To address these challenges, this paper proposes an innovative multi-dimensional feature enhancement and multi-scale feature adaptive aggregation and diffusion small object detection network (MFEAD-SODNet) for UAV aerial images. First, a backbone network integrating edge and spatial feature enhancement is developed to enhance feature representation from multiple perspectives, which improves small object recognition accuracy and detection performance. Second, the multi-scale feature adaptive aggregation and diffusion feature pyramid network (MFAD-FPN) is innovatively introduced. This network effectively preserves multi-scale information through adaptive feature fusion driven by channel selection. Additionally, it employs a cross-layer feature aggregation and adjacent layer feature diffusion mechanism to shorten feature transfer paths and minimize information propagation loss. Finally, a Lightweight shared detail-enhanced detection head is proposed to balance computational complexity while enhancing detailed feature representation. To evaluate the effectiveness of the proposed algorithm, experiments were conducted using VisDrone2019 as the baseline dataset. Results indicate that, compared to the baseline model, MFEAD-SODNet improves Mean Average Precision (mAP)@0.5 and [email protected]:0.95 by 7.6% and 5.1 %, respectively, while reducing the number of parameters by 23.3 %. Furthermore, the effectiveness and generalization of the MFEAD-SODNet model for small object detection were further validated using additional public and self-built datasets.