Improving object detection algorithm for unmanned aerial vehicle aerial images based on YOLOv8

In response to the challenges of diverse target scales, numerous similar objects, and target omissions and false positives resulting from object clustering in unmanned aerial vehicle (UAV) aerial image target identification, this paper introduces an improved UAV aerial image target identification algorithm called U-YOLOE . Within the initial YOLOv8 model's backbone network, a dual-route attention mechanism is incorporated to dynamically and sparsely filter out the least relevant features in the feature maps. This enhances the model's ability to capture key information in UAV aerial images, optimizing detector performance. Additionally, U-YOLOE employs WIoUv3 as the bounding box loss function, enhancing convergence speed and regression accuracy. Experimental results on the VisDrone2019 dataset reveal that, in comparison to the baseline model, U-YOLOE provides a 1% gain in Recall/%, a 1.2% gain in mAP0.5/%, and a 0.6% gain in mAP0.5:0.95%. Compared to other mainstream models, it demonstrates better performance in small object detection tasks for UAV aerial images.