湖南大学学报（自然科学版）2025，Vol.52Issue(4)：57-67,11.DOI:10.16339/j.cnki.hdxbzkb.2025266
面向小型无人机检测应用的改进YOLOv8算法
Improved YOLOv8 Algorithm for Small UAV Detection Applications
摘要
Abstract
The existing object detection algorithms,influenced by complex environments and the complexity of network models,face challenges in effectively detecting distant unmanned aerial vehicles(UAVs).This paper proposes an improved unmanned aerial vehicle(UAV)target detection algorithm based on YOLOv8.First,to address the challenge of detecting small unmanned aerial vehicle targets at long distances,a new ultra-small object detection layer is proposed,which integrates shallow features.In this approach,the largest target detection layer is removed to optimize target scale focus and reduce network complexity.Second,the Backbone part incorporates the GhostConv module to further decrease the model's parameter count.Then,in the Neck part,the LSKA attention mechanism is integrated by replacing the Bottleneck section in the C2f module with LSKA,designing a new C2f-LSKA module to replace some C2f modules in the Neck,enhancing the model's contextual awareness and spatial information processing ability.Lastly,WIoUv3 is used as the boundary loss function to further improve model accuracy.Experimental results show that,compared with the original model,the improved model increases precision(P)by 5.0%,recall(R)by 11.9%,and mAP@0.5 by 9.5%on a custom UAV dataset,while reduces the model's parameter count and size by 68.9%and 65.1%,respectively.关键词
无人机检测/YOLOv8/大型可分离卷积核/WIoUv3Key words
unmanned aerial vehicle(UAV)detection/YOLOv8/large-kernel separable convolution/WIoUv3分类
信息技术与安全科学引用本文复制引用
仲元昌,陈宇,杨子楚,李大林..面向小型无人机检测应用的改进YOLOv8算法[J].湖南大学学报（自然科学版）,2025,52(4):57-67,11.基金项目
国家自然科学基金资助项目(U22B2095),National Natural Science Foundation of China(U22B2095) （U22B2095）
国网重庆市电力公司电力科学研究院科技创新项目(H20221585),Science and Technology Innovation Project of State Grid Chongqing Electric Power Research Institute(H20221585) （H20221585）
