当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经...当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经网络算法(3D convolutional gait recognition network based on adaptFormer and spect-conv,3D-ASgaitNet)。首先,初始残差卷积层将二进制轮廓数据转换为浮点编码特征图,以提供密集的低级结构特征;在此基础上,光谱层通过频域和时域的联合处理增强特征提取能力,并使用伪3D残差卷积模块进一步提取高级时空特征;最后融合AdaptFormer模块,通过轻量级的下采样-上采样网络结构,以适应不同的数据分布和任务需求,提供灵活的特征变换能力。3D-ASgaitNet分别在4个公开的室内数据集(CASIA-B、OU-MVLP)、室外数据集(GREW、Gait3D)上进行,分别取得99.84%、87.83%、45.32%、72.12%的识别准确率。实验结果表明,所提出方法在CASIA-B、Gait3D数据集中的识别准确率接近SOTA性能。展开更多
Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
【背景】传统方法因静态感受野设计较难适配城市自动驾驶场景中汽车、行人及骑行者等目标的显著尺度差异,且跨尺度特征融合易引发层级干扰。【方法】针对自动驾驶场景中多类别、多尺寸目标的3D检测中跨尺度表征一致性的关键挑战,本研究...【背景】传统方法因静态感受野设计较难适配城市自动驾驶场景中汽车、行人及骑行者等目标的显著尺度差异,且跨尺度特征融合易引发层级干扰。【方法】针对自动驾驶场景中多类别、多尺寸目标的3D检测中跨尺度表征一致性的关键挑战,本研究提出基于均衡化感受野的3D目标检测方法VoxTNT,通过局部-全局协同注意力机制提升检测性能。在局部层面,设计了PointSetFormer模块,引入诱导集注意力模块(Induced Set Attention Block,ISAB),通过约简的交叉注意力聚合高密度点云的细粒度几何特征,突破传统体素均值池化的信息损失瓶颈;在全局层面,设计了VoxelFormerFFN模块,将非空体素抽象为超点集并实施跨体素ISAB交互,建立长程上下文依赖关系,并将全局特征学习计算负载从O(N^(2))压缩至O(M^(2))(M<<N,M为非空体素数量),规避了复杂的Transformer直接使用在原始点云造成的高计算复杂度。该双域耦合架构实现了局部细粒度感知与全局语义关联的动态平衡,有效缓解固定感受野和多尺度融合导致的特征建模偏差。【结果】实验表明,该方法在KITTI数据集单阶段检测下,中等难度级别的行人检测精度AP(Average Precision)值达到59.56%,较SECOND基线提高约12.4%,两阶段检测下以66.54%的综合指标mAP(mean Average Precision)领先次优方法BSAODet的66.10%。同时,在WOD数据集中验证了方法的有效性,综合指标mAP达到66.09%分别超越SECOND和PointPillars基线7.7%和8.5%。消融实验进一步表明,均衡化局部和全局感受野的3D特征学习机制能显著提升小目标检测精度(如在KITTI数据集中全组件消融的情况下,中等难度级别的行人和骑行者检测精度分别下降10.8%和10.0%),同时保持大目标检测的稳定性。【结论】本研究为解决自动驾驶多尺度目标检测难题提供了新思路,未来将优化模型结构以进一步提升效能。展开更多
文摘当前,步态识别的主流方法常依赖堆叠卷积层来逐步扩大感受野,以融合局部特征,这种方法大多采用浅层网络,在提取步态图像的全局特征时存在一定的局限性,并缺乏对时序周期特征信息的关注。因此提出一种融合Transformer和3D卷积的深层神经网络算法(3D convolutional gait recognition network based on adaptFormer and spect-conv,3D-ASgaitNet)。首先,初始残差卷积层将二进制轮廓数据转换为浮点编码特征图,以提供密集的低级结构特征;在此基础上,光谱层通过频域和时域的联合处理增强特征提取能力,并使用伪3D残差卷积模块进一步提取高级时空特征;最后融合AdaptFormer模块,通过轻量级的下采样-上采样网络结构,以适应不同的数据分布和任务需求,提供灵活的特征变换能力。3D-ASgaitNet分别在4个公开的室内数据集(CASIA-B、OU-MVLP)、室外数据集(GREW、Gait3D)上进行,分别取得99.84%、87.83%、45.32%、72.12%的识别准确率。实验结果表明,所提出方法在CASIA-B、Gait3D数据集中的识别准确率接近SOTA性能。
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
文摘【背景】传统方法因静态感受野设计较难适配城市自动驾驶场景中汽车、行人及骑行者等目标的显著尺度差异,且跨尺度特征融合易引发层级干扰。【方法】针对自动驾驶场景中多类别、多尺寸目标的3D检测中跨尺度表征一致性的关键挑战,本研究提出基于均衡化感受野的3D目标检测方法VoxTNT,通过局部-全局协同注意力机制提升检测性能。在局部层面,设计了PointSetFormer模块,引入诱导集注意力模块(Induced Set Attention Block,ISAB),通过约简的交叉注意力聚合高密度点云的细粒度几何特征,突破传统体素均值池化的信息损失瓶颈;在全局层面,设计了VoxelFormerFFN模块,将非空体素抽象为超点集并实施跨体素ISAB交互,建立长程上下文依赖关系,并将全局特征学习计算负载从O(N^(2))压缩至O(M^(2))(M<<N,M为非空体素数量),规避了复杂的Transformer直接使用在原始点云造成的高计算复杂度。该双域耦合架构实现了局部细粒度感知与全局语义关联的动态平衡,有效缓解固定感受野和多尺度融合导致的特征建模偏差。【结果】实验表明,该方法在KITTI数据集单阶段检测下,中等难度级别的行人检测精度AP(Average Precision)值达到59.56%,较SECOND基线提高约12.4%,两阶段检测下以66.54%的综合指标mAP(mean Average Precision)领先次优方法BSAODet的66.10%。同时,在WOD数据集中验证了方法的有效性,综合指标mAP达到66.09%分别超越SECOND和PointPillars基线7.7%和8.5%。消融实验进一步表明,均衡化局部和全局感受野的3D特征学习机制能显著提升小目标检测精度(如在KITTI数据集中全组件消融的情况下,中等难度级别的行人和骑行者检测精度分别下降10.8%和10.0%),同时保持大目标检测的稳定性。【结论】本研究为解决自动驾驶多尺度目标检测难题提供了新思路,未来将优化模型结构以进一步提升效能。
