生成对抗网络在汽车零部件识别中的应用

打开文本图片集

中图分类号：U469.72 文献标识码：A 文章编号：1003-8639（2025）08-0160-04

【Abstract】With thedevelopment of intelligent manufacturing and industrial automation，theautomotive industry has raisedtherequirements fortheacuracyand eficiencyofcomponent identification.Traditional imagerecognition methods have limitedaccuracyunder complex backgroundsand pose changes，making it dificult tomet production demands. Generativeadversarial networks，takingadvantageof their strengthsinimageenhancement andsynthesis，aregradually beingapplied totheimagepreprocesinganddataaugmentationofcomponentstoimprovetherobustnessofrecognition systems.Basedon the principleof GenerativeadversarialNetwork（GAN）and combined with theYOLOv8objectdetection algorithm，thisarticleconductsautomotivepartsrecognitionexperimentsthrough comparativetests，confusionmatrix analysisandothermethods toverifytheefectivenessofGANinimprovingrecognitionaccuracyandmodelgeneralization ability，andanalyzes theadvantagesandchalengesofitspracticalapplication.ResearchshowsthatGANcanffectively improve the accuracy and robustness of automotive parts recognition，increasing the recognition mAP@0.5 from 89.8% to 93.6% .Especially inthe scenarioof scarce samples，therobustnessissignificantlyenhanced，providing new technical support for related fields.

【KeyWords】generative adversarial network；YOLOv8；automotive parts；image recognition；data augmentation

0 引言

在智能制造和汽车生产自动化快速发展的背景下，零部件识别系统在生产检测、装配引导及智能质检等领域发挥着越来越重要的作用。（剩余3920字）