SFR-YOLO: small target detection algorithm for UAV imagery based on improved YOLOv8

doi:10.13474/j.cnki.11-2246.2025.0706

Abstract

Abstract: Addressing the issues of small targets in drone imagery having a low pixel ratio,leading to easy loss of features,as well as the large parameter count and deployment challenges of traditional object detection models,this paper proposes a lightweight small object detection algorithm named SFR-YOLO based on an improved YOLOv8.Firstly,this paper introduces a lightweight Shared detail-enhanced convolution detection head (SDCDH),which not only reduces the number of parameters in the detection head by sharing convolutions but also enhances the representation of detailed features by introducing detail-enhanced convolution (DEConv) in the shared layers.Secondly,the feature fusion network is improved using a weighted bidirectional feature pyramid network (BIFPN) with added shallow feature fusion branches and the removal of deep convolution,which boosts the detection performance for small objects.Finally,this paper designs a CRFA module that combines spatial attention and receptive field features to enhance the feature extraction capability of the model's backbone network.Experimental results demonstrate that SFR-YOLO achieves a 3.8%improvement in mean average precision (mAP) compared to the YOLOv8n algorithm on the VisDrone2019 dataset,SFR-YOLO not only enhances the detection of small objects but also meets the requirements for model deployment.Additionally,transfer experiments of SFR-YOLO on the CARPK dataset further validating the effectiveness of the proposed method in this paper.

Key words: small target detection, YOLOv8, drone imagery, lightweight

CLC Number:

P237

SUN Jiyuan, JI Song, GAO Ding, LI Kai, ZHANG Ruiying. SFR-YOLO: small target detection algorithm for UAV imagery based on improved YOLOv8[J]. Bulletin of Surveying and Mapping, 2025, 0(7): 32-39.

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