UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios

Unmanned aerial vehicle (UAV) object detection plays a crucial role in civil, commercial, and military domains. The proposed method effectively improves the ability to detect small objects.

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• 1 However, the high proportion of small objects in UAV images and the limited platform resources lead to the low accuracy of most of the existing detection models embedded in UAVs, and it is difficult to strike a good balance between detection performance and resource consumption.
• 2 To alleviate the above problems, we optimize YOLOv8 and propose an object detection model based on UAV aerial photography scenarios, called UAV-YOLOv8.
• 3 Firstly, Wise-IoU (WIoU) v3 is used as a bounding box regression loss, and a wise gradient allocation strategy makes the model focus more on common-quality samples, thus improving the localization ability of the model.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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