集成技术2026，Vol.15Issue(2)：91-103,13.DOI:10.12146/j.issn.2095-3135.20250320001
基于MobileNetV4改进的YOLOv8目标检测算法研究
Research on an Improved YOLOv8 Object Detection Algorithm Based on MobileNetV4
摘要
Abstract
The lightweight convolutional neural network designed for mobile devices features fast inference speed but is constrained by its inherent locality.Local information can only be captured within a windowed region,leading to performance degradation.Introducing the self-attention mechanism can capture global information,but it reduces detection speed.To address these issues,this paper introduces a hardware-friendly MobileNetV4 network architecture based on YOLOv8,incorporating a universally inverted bottleneck search block that integrates the inverted bottleneck,ConvNext,Feed Forward network,and a novel variant of extra depthwise convolution.Additionally,a dynamic upsampling operator is introduced to improve the upsampling operation,reducing GPU memory usage and latency in the model.Furthermore,this paper enhances the detection head of YOLOv8 by introducing a dynamic detection head,which combines spatial awareness,scale awareness,and task awareness into a unified framework.It effectively applies the attention mechanism in the object detection head,improving detection performance and efficiency.The experimental results demonstrate that compared to the next-best model,YOLOv8n,YOLOv8n_M achieved an improvement of 1.3%in mean average precision(mPA50～95).In terms of model complexity,YOLOv8n_M successfully compresses the model size by 36%(with a reduction of 1 million parameters)and reduces computational costs by 26%(The giga floating-point operations(GFLOPs)were reduced by 2.4).The proposed YOLOv8n_M effectively reduces the model's parameter count and inference time while improving object detection accuracy in various environments to a certain extent.关键词
YOLOv8/MobileNetV4/注意力机制/移动设备/动态检测头Key words
YOLOv8/MobileNetV4/attention mechanism/mobile devices/dynamic detection head分类
信息技术与安全科学引用本文复制引用
张宁,周云翀,徐坤财,左超超,彭如镜..基于MobileNetV4改进的YOLOv8目标检测算法研究[J].集成技术,2026,15(2):91-103,13.基金项目
贵阳信息科技学院教改课题项目(2024JG007) （2024JG007）
贵州省青年科技人才成长项目(黔教技[2024]279 号,黔教技[2024]278号) （黔教技[2024]279 号,黔教技[2024]278号）
贵州省教育科学规划课题青年课题项目(2024C018) This work is supported by Teaching Reform Project of Guiyang Institute of Information Science and Technology(2024JG007) （2024C018）
Youth Project of Guizhou Provincial Education Science Planning Program(QianJiaoJi[2024]No.279,QianJiaoJi[2024]No.278) （QianJiaoJi[2024]No.279,QianJiaoJi[2024]No.278）
Youth Project of Guizhou Provincial Education Science Planning Project(2024C018) （2024C018）
