甲骨文是汉字的源头和中华优秀传统文化的根脉。由于甲骨文拓片为人工雕刻且深埋地下,存在样本分布不均、噪声严重等问题,导致识别精度不高。针对以上问题,提出一种基于FM-MobileViT(Fusion and Attention Mechanism MobileViT)网络的...甲骨文是汉字的源头和中华优秀传统文化的根脉。由于甲骨文拓片为人工雕刻且深埋地下,存在样本分布不均、噪声严重等问题,导致识别精度不高。针对以上问题,提出一种基于FM-MobileViT(Fusion and Attention Mechanism MobileViT)网络的甲骨文字识别方法。首先,对数据集中图像进行锐化预处理操作,使目标边缘更清晰明显;并对数据数量过少的字符类别对应图像采用随机旋转、随机错切等方式进行数据增强,提升了数据集的质量,丰富了样本数据。其次,设计融合模块,构建跳转连接结构,将深浅层特征融合,使提取到的特征图能够融合浅层特征和语义特征;并在融合模块中引入CBAM注意力机制,使融合操作更有指向性、目的性,增强模型特征提取的能力。通过消融实验和对比实验表明,FM-MobileViT模型识别准确率达到92.3%,比MobileViT提升了1.7百分点,同时FPS达到30107。相比于同类型的网络结构,FM-MobileViT不仅有更高的准确率,而且取得了精度与速度的权衡。展开更多
Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order t...Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.展开更多
文摘甲骨文是汉字的源头和中华优秀传统文化的根脉。由于甲骨文拓片为人工雕刻且深埋地下,存在样本分布不均、噪声严重等问题,导致识别精度不高。针对以上问题,提出一种基于FM-MobileViT(Fusion and Attention Mechanism MobileViT)网络的甲骨文字识别方法。首先,对数据集中图像进行锐化预处理操作,使目标边缘更清晰明显;并对数据数量过少的字符类别对应图像采用随机旋转、随机错切等方式进行数据增强,提升了数据集的质量,丰富了样本数据。其次,设计融合模块,构建跳转连接结构,将深浅层特征融合,使提取到的特征图能够融合浅层特征和语义特征;并在融合模块中引入CBAM注意力机制,使融合操作更有指向性、目的性,增强模型特征提取的能力。通过消融实验和对比实验表明,FM-MobileViT模型识别准确率达到92.3%,比MobileViT提升了1.7百分点,同时FPS达到30107。相比于同类型的网络结构,FM-MobileViT不仅有更高的准确率,而且取得了精度与速度的权衡。
基金supported by the National Natural Science Foundation of China(Nos.62373215,62373219 and 62073193)the Natural Science Foundation of Shandong Province(No.ZR2023MF100)+1 种基金the Key Projects of the Ministry of Industry and Information Technology(No.TC220H057-2022)the Independently Developed Instrument Funds of Shandong University(No.zy20240201)。
文摘Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment.
