IFN-YOLOv8:Improved YOLOv8 Based on Information Fusion Network for Object Detection on Drone-captured Scenarios

Abstract

Object detection in drone-captured scenarios presents significant challenges due to factors such as varying object scales, motion blur, and dense object clusters. Although existing methods, including attention blocks and feature fusion networks, have shown improvements in detection accuracy, they often come with high computational costs, which hinder realtime performance. In this paper, we propose IFN-YOLOv8, an enhanced version of YOLOv8, designed to address these challenges. By integrating the P2 feature scale, IFN-YOLOv8 enhances small object detection through higher-resolution feature maps. Additionally, we introduce a novel convolutional block, RHAConv, to replace traditional convolution layers, improving feature representation in scenes with dense object clusters. A new Information Fusion Module is also proposed to refine object features, reducing both missed and false detections. Experimental results on the VisDrone and DOTA datasets demonstrate that IFN-YOLOv8 outperforms mainstream methods, achieving an mAP@50 of 45.7% and 68.5%, respectively, while maintaining low resource consumption and high detection speed.