Abstract: To address the issues of low detection accuracy, frequent missed detections, and false detections of small objects in unmanned aerial vehicle (UAV) aerial images, this paper proposes an improved small-object detection model named MDH-YOLOv8. Firstly, the Focal-EIoU loss function is employed to replace the CIoU Loss, resolving the problem of inaccurate regression results. A Small- object feature information extraction SAE(self-attention information extraction) module is designed to mitigate the insufficient information extraction of the spatial pyramid pooling fast (SPPF) module, enabling the model to simultaneously focus on multiple key small-object regions within the image. Secondly, a C2f_DCN module adaptable to complex geometric deformations is introduced, where deformable convolutions are fused with multiple iterations of bottleneck layers to enhance the robustness of the detection model. Finally, a dedicated small-object detection head (STDH) module is added to reduce the false detection and missed detection rates of small objects, thereby improving detection accuracy. Experimental results on the VisDrone2019 and DOTA datasets demonstrate that the MDH-YOLOv8 model achieves a 4.2 percentage point increase in mAP@0.5 and a 3.4 percentage point  increase in mAP@ 0.5:0.95 compared to the YOLOv8 model. Compared to mainstream models for small-object detection, the MDH-YOLOv8 model improves detection accuracy for small objects while maintaining a lightweight design.

Key words: small object detection, YOLOv8 model, space pyramid pool, deformable convolution, small object detection layer