In drone aerial photography applications, small object detection is crucial. For instance, it enables locating missing individuals on the ground during search-and-rescue operations, identifying distant vehicles in traffic monitoring, and detecting early-stage pest infestations in agricultural fields. However, aerial images present a unique challenge: due to the high flight altitude of drones, targets occupy only a minimal pixel area. Combined with complex backgrounds and sparse features, small objects are easily obscured by surrounding environments. To address these issues, this paper proposes the EFPN-YOLO model based on YOLOv12n. First, we introduce the Feature-Sharing Convolution (FSConv) module, which extracts multi-scale features with low parameter requirements through shared convolution kernels and multi-scale sparse sampling. Second, by integrating deformable convolutions with a dual-channel attention mechanism, we develop the Enhanced Dual-Dimensional Calibration (EDDC) module, significantly improving spatial feature modeling capabilities and feature enhancement effectiveness. Finally, we construct the RC-FPN architecture, employing a bidirectional fusion structure and diagonal cross-layer skip connections to minimize information loss. Meanwhile, the Bottleneck structure in the C3K2 module is replaced with the RepViTBlock to construct the C3k2RVB module, which enhances the multi-scale feature expression ability through a two-stage design of spatial and channel mixing. On the VisDrone2019 dataset, the model’s mAP50 improved from 33. 9% to 40. 7%; on the TinyPerson dataset, it rose from 13. 9% to 19. 2%; and on the NVIDIA Jetson Orin Nano 8 GB superplatform, the model achieved a frame rate (FPS) of 15. Experiments demonstrate that EFPN-YOLO excels in small object detection and holds significant practical value.
Li et al. (2026) studied this question.