吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (5): 1205-1213.doi: 10.13229/j.cnki.jdxbgxb.20231081

• 车辆工程·机械工程 • 上一篇    下一篇

汽车漆面缺陷高精度检测系统

陆玉凯1,2(),袁帅科3,熊树生1,4(),朱绍鹏1,张宁3   

  1. 1.浙江大学 动力机械及车辆工程研究所，杭州 310014
    2.浙江吉利汽车有限公司，杭州 310051
    3.燕山大学 机械工程学院，河北 秦皇岛 066004
    4.龙泉产业创新研究院，浙江 龙泉 323700
  • 收稿日期:2023-10-10 出版日期:2024-05-01 发布日期:2024-06-11
  • 通讯作者: 熊树生 E-mail:yukai.lu@geely.com;xiongss@zju.edu.cn
  • 作者简介:陆玉凯（1980-），男，高级工程师，博士研究生. 研究方向：机器视觉，缺陷检测，汽车质量控制.E-mail： yukai.lu@geely.com
  • 基金资助:
    工信部重点领域及特定场景工业互联网平台应用项目(TC200802D);工信部5G+工业互联网”高质量网络和公共服务平台-离散行业高质量网络项目(TC200A00N)

High precision detection system for automotive paint defects

Yu-kai LU1,2(),Shuai-ke YUAN3,Shu-sheng XIONG1,4(),Shao-peng ZHU1,Ning ZHANG3   

  1. 1.Power Machinery & Vehicular Engineering Institute，Zhejiang University，Hangzhou 310014，China
    2.Zhejiang Geely Automobile Co. ，Ltd. ，Hangzhou 310051，China
    3.College of Mechanical Engineering，Yanshan University，Qinhuangdao 066004，China
    4.Longquan Industrial Innovation Research Institute，Longquan 323700，China
  • Received:2023-10-10 Online:2024-05-01 Published:2024-06-11
  • Contact: Shu-sheng XIONG E-mail:yukai.lu@geely.com;xiongss@zju.edu.cn

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摘要：

汽车涂装过程中产生的漆面缺陷影响着整车外观质量，针对人工检测存在漏检、低效以及传统检测方案的高实施成本等问题，提出了一种基于改进YOLOv7算法的汽车漆面缺陷检测系统。构建了汽车漆面缺陷数据集，共有4023张图像，其中包含5种常见汽车漆面缺陷；针对YOLOv7算法在微小缺陷上检测精度不足的问题，在原网络中引入了GAM注意力机制和SPPFCSPC模块，用于提高算法对微小缺陷特征的提取能力，同时采用改进的ELAN模块对网络结构进行改进，减少网络过深造成的小目标信息丢失问题，保证在减轻网络模型的同时提高网络对微小特征的识别精度；实验结果表明：本文方法大幅提升了对微小漆面缺陷的检测性能，缺陷的平均检测精度达到了88.9%，与多种算法相比检测精度最高。

关键词: 车辆工程, 汽车漆面, 缺陷检测, 深度学习

Abstract:

The paint defects that exist during the automotive painting process affect the overall appearance quality of the car. In response to the problems of missed inspection，low efficiency，and high implementation cost of traditional inspection schemes in manual inspection，a paint defect detection method based on the improved YOLOv7 algorithm is proposed. A dataset of automotive paint defects was constructed，consisting of 4023 images，including 5 types of automotive paint defects； In response to the problem of insufficient detection accuracy of YOLOv7 algorithm on small defects，GAM attention mechanism and SPPFCSPC module were introduced into the original network to improve the algorithm's ability to extract small defect features. At the same time，an improved ELAN module was used to improve the network structure to reduce the problem of small target information loss caused by deep network，ensuring that the network model is reduced while improving the recognition accuracy of small features； Based on the constructed dataset，the defect detection performance of different algorithms was tested and the effectiveness of the module was verified. The experimental results show that this method significantly improves the detection ability of small defects on paint surfaces，with an average detection accuracy of 88.9%，which is the highest detection accuracy compared to various algorithms.

Key words: vehicle engineering, automotive paint surface, defect detection, deep learning

中图分类号: 

  • TP183

图1

缺陷图像采集平台"

表1

设备配置参数"

名称配置
工业相机Basler acA5472-5gc
相机分辨率5472×3648
像元尺寸2.4 μm×2.4 μm
镜头大恒HN-P-1624-25M-C1.2/1
工作距离450 mm
工业光源开孔面光源

图2

YOLOv7 目标检测算法网络结构图"

图3

ELAN模块"

图4

SPPCSPC模块"

图5

GAM注意力机制"

图6

SPPFCSPC模块"

图7

ELAN-S模块"

表2

实验环境参数"

参数配置
CPUIntel Xeon Gold 6271C @2.6 GHz×20
GPUTesla V100 SXM2；32 G
CUDA10.2
系统Ubuntu 18.04.6LTS
语言Python 3.8

图8

不同数量ELAN-S的精度对比"

表3

改进YOLOv7算法的消融实验"

GAMSPPFCSPCELAN-SmAP@0.5
///0.719
√//0.811
√√/0.830
√√√0.889

图9

不同模型的热力图"

图10

不同缺陷的检测结果"

表4

不同检测方法的对比结果"

算法mAP@0.5mAP@0.5∶0.95Time/ms
Faster-RCNN0.8090.424119.1
YOLOv40.7060.34924.4
YOLOR0.8010.42324.3
YOLOv70.7190.36715.0
改进YOLOv70.8890.47116.3
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