SOD-YOLO: Small-Object-Detection Algorithm Based on Improved YOLOv8 for UAV Images

The rapid development of unmanned aerial vehicle (UAV) technology has contributed to the increasing sophistication of UAV-based object-detection systems, which are now extensively utilized in civilian and military sectors. However, object detection from UAV images has numerous challenges, including significant variations in the object size, changing spatial configurations, and cluttered backgrounds with multiple interfering elements. To address these challenges, we propose SOD-YOLO, an innovative model based on the YOLOv8 model, to detect small objects in UAV images. The model integrates the receptive field convolutional block attention module (RFCBAM) in the backbone network to perform downsampling, improving…

• KT
  Key Technology Research and Development Program of Shandong

  Awards: 2021YFB1407005, 2021SFGC0401
• NK
  National Key Research and Development Program of China

  Awards: 2021SFGC0401, 2021YFB1407005