Multi-Object Detection Algorithm for UAV Aerial Images based on Improved YOLOV8

For the problem of multi-object detection in UAV aerial images, this paper presents an improved YOLOv8 object detection algorithm named ASF-WIoU-YOLOv8. Firstly, on the infrastructure of YOLOv8, an attention-scale sequence fusion mechanism (Attentional Scale Sequence Fusion-ASF) is added, which can integrate feature maps at different scales, so as to obtain better image features and extract richer and more accurate feature information. Then, the loss function is improved by using the Wise- IoU mechanism, which improves the flexibility and robustness of target detection by adaptively adjusting the weight coefficients. Wise-IoU can further improve the detection accuracy of the algorithm. The experimental results show that the average accuracy of the proposed algorithm is 2.0% higher than the YOLOv8 algorithm, which has higher detection accuracy on aerial images of UAV.