Abstract: To address the issues of low detection accuracy and large model size in existing object detection algorithms for UAV-captured images,this paper proposes an improved YOLOv8-based object detection algorithm named BF-YOLO.Firstly,the output detection layer of the network is reconstructed to enhance its capability for detecting small objects.Secondly,receptive field attention convolution is introduced to replace the standard convolution,enabling the network to focus on object location information and improving its ability to learn object features.Additionally,a multi-scale feature extraction module is designed,utilizing multiple grouped convolution units to capture object information at different receptive fields,thereby reducing the number of parameters while improving detection accuracy.Finally,a weighted bidirectional feature fusion method is incorporated into the neck network to enhance multi-scale feature fusion,boosting the model’s ability to recognize objects of varying scales.Experimental results on the VisDrone-DET2019 dataset demonstrate that the improved algorithm achieves a 7.3% increase in mAP50 compared to YOLOv8s,while reducing the model’s parameter count by 67.1%,effectively balancing detection accuracy and model lightweightness.

Key words: UAV image, small object detection, YOLOv8, feature fusion