DAFPN-YOLO: An Improved UAV-Based Object Detection Algorithm Based on YOLOv8s  

作　　者：Honglin Wang Yaolong Zhang Cheng Zhu 

机构地区：[1]School of Artificial Intelligence,Nanjing University of Information Science and Technology,Nanjing,210044,China [2]School of Computer Science,Nanjing University of Information Science and Technology,Nanjing,210044,China [3]Electrical&Computer Engineering,University of Illinois at Urbana-Champaign,Urbana,IL 61801,USA

出　　处：《Computers, Materials & Continua》2025年第5期1929-1949,共21页计算机、材料和连续体(英文)

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.62101275 and 62101274).

摘　　要：UAV-based object detection is rapidly expanding in both civilian and military applications,including security surveillance,disaster assessment,and border patrol.However,challenges such as small objects,occlusions,complex backgrounds,and variable lighting persist due to the unique perspective of UAV imagery.To address these issues,this paper introduces DAFPN-YOLO,an innovative model based on YOLOv8s(You Only Look Once version 8s).Themodel strikes a balance between detection accuracy and speed while reducing parameters,making itwell-suited for multi-object detection tasks from drone perspectives.A key feature of DAFPN-YOLO is the enhanced Drone-AFPN(Adaptive Feature Pyramid Network),which adaptively fuses multi-scale features to optimize feature extraction and enhance spatial and small-object information.To leverage Drone-AFPN’smulti-scale capabilities fully,a dedicated 160×160 small-object detection head was added,significantly boosting detection accuracy for small targets.In the backbone,the C2f_Dual(Cross Stage Partial with Cross-Stage Feature Fusion Dual)module and SPPELAN(Spatial Pyramid Pooling with Enhanced LocalAttentionNetwork)modulewere integrated.These components improve feature extraction and information aggregationwhile reducing parameters and computational complexity,enhancing inference efficiency.Additionally,Shape-IoU(Shape Intersection over Union)is used as the loss function for bounding box regression,enabling more precise shape-based object matching.Experimental results on the VisDrone 2019 dataset demonstrate the effectiveness ofDAFPN-YOLO.Compared to YOLOv8s,the proposedmodel achieves a 5.4 percentage point increase inmAP@0.5,a 3.8 percentage point improvement in mAP@0.5:0.95,and a 17.2%reduction in parameter count.These results highlight DAFPN-YOLO’s advantages in UAV-based object detection,offering valuable insights for applying deep learning to UAV-specific multi-object detection tasks.

关 键 词：YOLOv8 UAV-based object detection AFPN small-object detection head SPPELAN DualConv loss function 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]