摘要
基于机器视觉的白车身焊点自动化检测为车身焊接质量控制提供了有效的途径,然而受环境光污染的影响,焊点自动化检测装备的机器视觉系统较难进行准确定位。为解决传统的图像处理方法受环境干扰大及鲁棒性差的问题,提出一种基于深度学习的焊点位置检测方法。引入MobileNetv2的卷积结构代替YOLOv2的卷积层,并借鉴YOLOv2的细粒度特征的方法,解决YOLOv2模型参数较多的问题。采用GIoU loss对模型的损失函数进行改进,利用K-means聚类算法得到适合焊点数据集的anchor,从而获得高可靠性的轻量化白车身焊点位置检测模型FGM_YOLO。在白车身焊点测试集上进行测试,结果表明,与原YOLOv2模型相比,该模型的平均精度提升了2.47%,模型参数约为原模型的1/16,检测速度提高2倍,大幅提高了检测效率。
The machine-vision-based automatic detection methods for the welding spots of body in white provides an effective way to control the quality of the welding.However,due to the influence of light pollution,the machine vision system of the automatic detection equipment often fail to locate the welding spots accurately.To address the interference from environment and the weak robustness of the traditional methods,this paper proposes a method for welding spot detection based on deep learning.The method introduces the convolutional structure of MobileNetv2 to replace the convolutional layer of YOLOv2,and draws on the fine-grained feature of YOLOv2 to solve the excessive number of parameters of the traditional YOLOv2 model.Then GIoU loss is used to improve the loss function of the model.Finally,the K-means clustering algorithm is used to obtain a suitable anchor for the welding spot dataset,and an efficient and reliable welding spot detection model FGM_YOLO for light body in white is obtained.Results of testing and comparison on the test set of welding spots of body in white show that the AP of this model is 2.47%higher than that of YOLOv2.The number of its parameters is about 1/16 of that of the original model,and its detection speed is two times that of the original model,which proves a significant increase in the detection accuracy and efficiency.
作者
何智成
王振兴
HE Zhicheng;WANG Zhenxing(State Key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University,Changsha 410082,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第11期246-254,共9页
Computer Engineering
基金
国家自然科学基金创新研究群体项目(51621004)
国家自然科学基金(U1864207)
广西科技大学广西汽车零部件与整车技术重点实验室开放课题(2017GKLACVTKF01)
长沙市科技计划(kq1907104)。
