Enhancing small object detection in UAV images with DSD-YOLO

Small object detection is a critical task in computer vision, with extensive applications in UAV-based target detection and aerial image analysis. However, current small object detection algorithms often exhibit deficiencies in detection accuracy, leading to frequently missed detections and false positives. To address these challenges, we propose a method for an improved detection model based on YOLOv8s, namely DSD-YOLO. Our contributions are as follows: (a) Replacement of the original feature fusion module with a Convolutional Branch Attention module (C2fDA), which effectively enhances the model's ability to capture and utilize small object information. (b) Introducing a Small Object Detection Layer (SDₗayer) to facilitate multi-scale feature fusion, thereby improving the detection performance for small objects. (c) Incorporating the Dyhead detection head to flexibly capture effective feature information for small objects. Experimental results on the public VisDrone2019 dataset demonstrate our method enhances precision and recall by 6. 9% and 8. 5%, respectively, with mAP50 and mAP50: 95 increases by 9. 1% and 6. 3%, and detection speed (FPS) increases by 12. 6. The enhanced model demonstrates superior performance in detecting small objects in UAV images.