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基于改进YOLOv8的车辆漆面缺陷检测

郝友胜 文贞慧 冯小溪 邓泽华 黄清宝

计算机工程2026,Vol.52Issue(4):252-263,12.
计算机工程2026,Vol.52Issue(4):252-263,12.DOI:10.19678/j.issn.1000-3428.0070032

基于改进YOLOv8的车辆漆面缺陷检测

Vehicle Paint Defect Detection Based on Improved YOLOv8

郝友胜 1文贞慧 1冯小溪 1邓泽华 1黄清宝1

作者信息

  • 1. 广西大学电气工程学院,广西南宁 530000
  • 折叠

摘要

Abstract

To address the issues of low accuracy in vehicle paint defect detection,excessive parameters in detection algorithms,and the uneven distribution of easy and hard samples,a vehicle paint detection method based on an improved YOLOv8 is proposed.To enhance scratch defect detection capabilities and reduce model size,a Deformable Attention Transformer(DAT)mechanism is introduced into the backbone network,and Ghost Convolution(GhostConv)replaces the standard Convolution(Conv)modules.Subsequently,to improve feature extraction capabilities and further reduce model size,a C2f Based on Efficient Multiscale Attention(EMA)(C2f-E)module is proposed by combining the FasterBlock module and the EMA attention mechanism.Moreover,to enhance the detection performance for small objects,a network based on the Bidirectional Feature Pyramid Network(BiFPN)is designed.Additionally,by adding a small-object detection head and a multiscale feature fusion branch,a neck pyramid structure named BiFPN with Small Object Detection Head(BiFPN-D)is proposed.Finally,to address the balance issue between difficult and easy samples and improve the detection performance for small object defects,Wise-Intersection over Union version 3(WIoUv3)is employed as the loss function for training the network.The improved network is trained on a self-built dataset of vehicle paint defect images and subjected to comparative experiments.The results show that,the improved model achieves an increase of 5.5 percentage points in terms of mean Average Precision(mAP@0.5)and a reduction of 1.4× 106 in terms of parameter count,compared to YOLOv8n.

关键词

YOLOv算法/车辆漆面缺陷/目标检测/双向特征金字塔网络/损失函数

Key words

YOLOv8 algorithm/vehicle paint defect/object detection/Bidirectional Feature Pyramid Network(BiFPN)/loss function

分类

信息技术与安全科学

引用本文复制引用

郝友胜,文贞慧,冯小溪,邓泽华,黄清宝..基于改进YOLOv8的车辆漆面缺陷检测[J].计算机工程,2026,52(4):252-263,12.

基金项目

国家自然科学基金(62276072) (62276072)

广西研究生教育创新计划项目(JGY2023016) (JGY2023016)

广西大学研究生教育教学改革计划. ()

计算机工程

1000-3428

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