SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery
This addresses the challenge of detecting small objects in drone imagery, which is crucial for applications like surveillance and monitoring, but it is incremental as it builds on existing YOLO methods.
The paper tackled the problem of small object detection in UAV imagery by proposing SOD-YOLO, an enhanced YOLOv8-based model, which achieved a 36.1% increase in mAP$_{50:95}$ and a 20.6% increase in mAP$_{50}$ on the VisDrone2019-DET dataset compared to the baseline.
Small object detection remains a challenging problem in the field of object detection. To address this challenge, we propose an enhanced YOLOv8-based model, SOD-YOLO. This model integrates an ASF mechanism in the neck to enhance multi-scale feature fusion, adds a Small Object Detection Layer (named P2) to provide higher-resolution feature maps for better small object detection, and employs Soft-NMS to refine confidence scores and retain true positives. Experimental results demonstrate that SOD-YOLO significantly improves detection performance, achieving a 36.1% increase in mAP$_{50:95}$ and 20.6% increase in mAP$_{50}$ on the VisDrone2019-DET dataset compared to the baseline model. These enhancements make SOD-YOLO a practical and efficient solution for small object detection in UAV imagery. Our source code, hyper-parameters, and model weights are available at https://github.com/iamwangxiaobai/SOD-YOLO.