An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect s...An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.展开更多
金属表面焊缝缺陷的准确检测是确保工件安全使用的前提,由于缺陷与母材颜色相近、图像不清晰等情况,使用常规的2DRGB视觉难以完全检测出所有的缺陷类别,需要添加深度信息来辅助检测.试验提出一种基于RGB-D数据特征融合的焊缝表面缺陷检...金属表面焊缝缺陷的准确检测是确保工件安全使用的前提,由于缺陷与母材颜色相近、图像不清晰等情况,使用常规的2DRGB视觉难以完全检测出所有的缺陷类别,需要添加深度信息来辅助检测.试验提出一种基于RGB-D数据特征融合的焊缝表面缺陷检测方法,在YOLOv8网络模型的基础上,利用改进的对称主干网络结构提取RGB和深度特征的有效特征层,引入RGB-D数据特征融合模块,实现了RGB特征和深度特性在空间与通道位置的融合,在YOLOv8模型中加入CIoUNMS(complete intersection over union-non max suppression)非极大值抑制模块,提高了检验框的准确度.针对随机包含有烧穿、飞溅、焊瘤和气孔4个类别焊缝缺陷的图像进行了试验,结果表明,改进的YOLOv8比YOLOv8漏检率下降了17.84%,误检率下降了19.46%,证明了所述方法的有效性与准确性.展开更多
Light guide plate(LGP)is a kind of material used in the backlight module.How to improve the quality control of LGP has become the focus of research in the industry.To address issues such as low gray contrast and a hig...Light guide plate(LGP)is a kind of material used in the backlight module.How to improve the quality control of LGP has become the focus of research in the industry.To address issues such as low gray contrast and a high proportion of small target defects in LGP images,an improved you only look once version 5(YOLOv5)neural network based on multi-scale dilation convolution and a novel loss function is proposed.展开更多
基金supported by the Jiangsu Province IUR Cooperation Project (No.BY2021258)the Wuxi Science and Technology Development Fund Project (No.G20212028)。
文摘An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.
文摘金属表面焊缝缺陷的准确检测是确保工件安全使用的前提,由于缺陷与母材颜色相近、图像不清晰等情况,使用常规的2DRGB视觉难以完全检测出所有的缺陷类别,需要添加深度信息来辅助检测.试验提出一种基于RGB-D数据特征融合的焊缝表面缺陷检测方法,在YOLOv8网络模型的基础上,利用改进的对称主干网络结构提取RGB和深度特征的有效特征层,引入RGB-D数据特征融合模块,实现了RGB特征和深度特性在空间与通道位置的融合,在YOLOv8模型中加入CIoUNMS(complete intersection over union-non max suppression)非极大值抑制模块,提高了检验框的准确度.针对随机包含有烧穿、飞溅、焊瘤和气孔4个类别焊缝缺陷的图像进行了试验,结果表明,改进的YOLOv8比YOLOv8漏检率下降了17.84%,误检率下降了19.46%,证明了所述方法的有效性与准确性.
基金supported by the Development of Intelligent Sensing and Control System for Industrial Robots(No.BY2021258)the R&D and Industrialization of Complete Equipment for Automated Precision Assembly and Testing of Liquid Backplate Optical Modules(No.G20212028)。
文摘Light guide plate(LGP)is a kind of material used in the backlight module.How to improve the quality control of LGP has become the focus of research in the industry.To address issues such as low gray contrast and a high proportion of small target defects in LGP images,an improved you only look once version 5(YOLOv5)neural network based on multi-scale dilation convolution and a novel loss function is proposed.
