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

作　　者：仲元昌 陈宇 杨子楚 李大林 ZHONG Yuanchang;CHEN Yu;YANG Zichu;LI Dalin(School of Electrical Engineering,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学电气工程学院,重庆400044

出　　处：《湖南大学学报(自然科学版)》2025年第4期57-67,共11页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(U22B2095);国网重庆市电力公司电力科学研究院科技创新项目(H20221585)。

摘　　要：现有的目标检测算法对小型无人机存在难以有效检测、受复杂环境影响以及网络模型复杂等问题,为此提出一种基于YOLOv8的改进型无人机目标检测算法.首先,针对远距离飞行的无人机目标较小的问题,添加一个融合了浅层特征的新的极小目标检测层,同时剔除最大目标检测层,以实现优化目标尺度聚焦并降低网络的复杂度;其次,在Backbone网络中引入GhostConv模块,进一步减少模型的参数量,然后,在Neck网络中融合LSKA模块中的注意力机制,将C2f模块中的Bottleneck用LSKA进行替换,设计全新的C2f-LSKA模块代替Neck中的C2f模块,提高模型对上下文的感知能力和对空间信息的处理能力;最后,采用WIoUv3作为边界损失函数,进一步提高模型精度.实验结果表明,与原模型相比,改进的模型在自建无人机数据集上的精确度P提升了5.0个百分点,召回率R提升了11.9个百分点,mAP@0.5提升了9.5个百分点,改进后的模型参数数量和模型大小分别降低了68.9个百分点和65.1个百分点.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 WIoUv3 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]