SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery

• URL: http://arxiv.org/abs/2507.12727v1
• Date: Thu, 17 Jul 2025 02:04:54 GMT
• Title: SOD-YOLO: Enhancing YOLO-Based Detection of Small Objects in UAV Imagery
• Authors: Peijun Wang, Jinhua Zhao,
• Abstract summary: Experimental results demonstrate that SOD-YOLO significantly improves detection performance.<n>SOD-YOLO is a practical and efficient solution for small object detection in UAV imagery.
• Score: 5.639904484784127
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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