DBYOLOv8: Dual-Branch YOLOv8 Network for Small Object Detection on Drone Image

Object detection based on drone platforms is a valuable yet challenging research field. Although general object detection networks based on deep learning have achieved breakthroughs in natural scenes, drone images in urban environments often exhibit characteristics such as a high proportion of small objects, dense distribution, and significant scale variations, posing significant challenges for accurate detection. To address these issues, this paper proposes a dual-branch object detection algorithm based on YOLOv8 improvements. Firstly, an auxiliary branch is constructed by extending the YOLOv8 backbone to aggregate high-level semantic information within the network, enhancing the feature extraction capability. Secondly, a Multi-Branch Feature Enhancement (MBFE) module is designed to enrich the feature representation of small objects and enhance the correlation of local features. Third, Spatial-to-Depth Convolution (SPDConv) is utilized to mitigate the loss of small object information during downsampling, preserving more small object feature information. Finally, a dual-branch feature pyramid is designed for feature fusion to accommodate the dual-branch input. Experimental results on the VisDrone benchmark dataset demonstrate that DBYOLOv8 outperforms state-of-the-art object detection methods. Our proposed DBYOLOv8s achieve mAP@0.5 of 49.3% and mAP@0.5:0.95 of 30.4%, which are 2.8% and 1.5%higher than YOLOv9e, respectively.

@article{Tan2025,
  title     = {DBYOLOv8: Dual-Branch YOLOv8 Network for Small Object Detection on Drone Image},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {1},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yawei Tan and Bingxin Xu and Jiangsheng Sun and Cheng Xu and Weiguo Pan and Songyin Dai and Hongzhe Liu},
  doi       = {10.14569/IJACSA.2025.01601124},
  url       = {https://doi.org/10.14569/IJACSA.2025.01601124}
}