计算机工程2025，Vol.51Issue(5)：314-325,12.DOI:10.19678/j.issn.1000-3428.0069122
基于改进YOLOv8的轻量化车辆检测网络
Lightweight Vehicle Detection Network Based on Improved YOLOv8
摘要
Abstract
The current high-precision vehicle detection model faces challenges due to its excessive parameterization and computational demands,making it unsuitable for efficient operation on intelligent transportation devices.Conversely,lightweight vehicle detection models often sacrifice accuracy,rendering them unsuitable for practical tasks.In response,an improved lightweight vehicle detection network based on YOLOv8 is proposed.This enhancement involves substituting the main network with the FasterNet architecture,which reduces the computational and memory access requirements.Additionally,we replace the Bidirectional Feature Pyramid Network(BiFPN)in the neck with a weighted bidirectional feature pyramid network to simplify the feature fusion process.Simultaneously,we introduce a dynamic detection head with a fusion attention mechanism to achieve nonredundant integration of the detection head and attention.Furthermore,we address the deficiencies of the Complete Intersection over Union(CIoU)in terms of detection accuracy and convergence speed by proposing a regression loss algorithm that incorporates the Scale-invariant Intersection over Union(SIoU)combined with the Normalized Gaussian Wasserstein Distance(NWD).Finally,to minimize the computational demands on edge devices,we implement amplitude-based layer-wise adaptive sparsity pruning,which further compresses the model size.Experimental results demonstrate that the proposed improved model,compared with the original YOLOv8s model,achieves a 1.5 percentage points increase in accuracy,a 78.9％reduction in parameter count,a 67.4％decrease in computational demands,and a 77.8％reduction in model size.This demonstrates the outstanding lightweight effectiveness and practical utility of the proposed model.关键词
YOLOv8模型/车辆检测/轻量化/FasterNet网络/归一化高斯Wasserstein距离Key words
YOLOv8 model/vehicle detection/lightweight/FasterNet network/Normalized Gaussian Wasserstein Distance(NWD)分类
计算机与自动化引用本文复制引用
陈梓延,王晓龙,何迪,安国成..基于改进YOLOv8的轻量化车辆检测网络[J].计算机工程,2025,51(5):314-325,12.基金项目
"十四五"国家重点研发计划(2023YFC3006700) （2023YFC3006700）
国家自然科学基金(61971278,62231010). （61971278,62231010）
