Gait recognition,a promising biometric technology,relies on analyzing individuals' walking patterns and offers a non-intrusive and convenient approach to identity verification.However,gait recognition accuracy is ...Gait recognition,a promising biometric technology,relies on analyzing individuals' walking patterns and offers a non-intrusive and convenient approach to identity verification.However,gait recognition accuracy is often compromised by external factors such as changes in viewpoint and attire,which present substantial challenges in practical applications.To enhance gait recognition performance under diverse viewpoints and complex conditions,a global-local part-shift network is proposed in this paper.This framework integrates two novel modules:the part-shift feature extractor and the dynamic feature aggregator.The part-shift feature extractor strategically shifts body parts to capture the intrinsic relationships between non-adjacent regions,enriching the recognition process with both global and local spatial features.The dynamic feature aggregator addresses long-range dependency issues by incorporating multi-range temporal modeling,effectively aggregating information across parts and time steps to achieve a more robust recognition outcome.Comprehensive experiments on the CASIA-B dataset demonstrate that the proposed global-local part-shift network delivers superior performance compared with state-of-the-art methods,highlighting its potential for practical deployment.展开更多
针对X射线安检场景中违禁品目标检测精度低,检测模型过于复杂的问题,在YOLOv7-Tiny模型的基础上,提出了一种新的轻量化检测方法。首先在骨干网络中融合改进的轻量化模块GhostNetV2,在减少模型参数的同时,提高训练效率;其次在YOLOv7-Tin...针对X射线安检场景中违禁品目标检测精度低,检测模型过于复杂的问题,在YOLOv7-Tiny模型的基础上,提出了一种新的轻量化检测方法。首先在骨干网络中融合改进的轻量化模块GhostNetV2,在减少模型参数的同时,提高训练效率;其次在YOLOv7-Tiny的颈部网络部分加入金字塔拆分注意力机制,有效解决参数减少导致的提取特征不足问题,提高背景复杂以及多尺度目标回归的准确性;最后,通过使用归一化Wasserstein距离方法来度量损失,替代了原有的Intersection over Union度量,降低了小目标位置偏差的敏感性,增强了小目标的回归准确性。实验结果表明,改进模型在SIXray、CLCXray和OPIXray数据集上平均检测精度达到92.9%、76.2%和91.2%,相比原始算法分别提升了6.5%、2%和1.8%;所提出模型在轻量化的同时能够进一步提高检测能力,可以满足实时检测要求,具有较好的应用价值。展开更多
文摘Gait recognition,a promising biometric technology,relies on analyzing individuals' walking patterns and offers a non-intrusive and convenient approach to identity verification.However,gait recognition accuracy is often compromised by external factors such as changes in viewpoint and attire,which present substantial challenges in practical applications.To enhance gait recognition performance under diverse viewpoints and complex conditions,a global-local part-shift network is proposed in this paper.This framework integrates two novel modules:the part-shift feature extractor and the dynamic feature aggregator.The part-shift feature extractor strategically shifts body parts to capture the intrinsic relationships between non-adjacent regions,enriching the recognition process with both global and local spatial features.The dynamic feature aggregator addresses long-range dependency issues by incorporating multi-range temporal modeling,effectively aggregating information across parts and time steps to achieve a more robust recognition outcome.Comprehensive experiments on the CASIA-B dataset demonstrate that the proposed global-local part-shift network delivers superior performance compared with state-of-the-art methods,highlighting its potential for practical deployment.
文摘针对X射线安检场景中违禁品目标检测精度低,检测模型过于复杂的问题,在YOLOv7-Tiny模型的基础上,提出了一种新的轻量化检测方法。首先在骨干网络中融合改进的轻量化模块GhostNetV2,在减少模型参数的同时,提高训练效率;其次在YOLOv7-Tiny的颈部网络部分加入金字塔拆分注意力机制,有效解决参数减少导致的提取特征不足问题,提高背景复杂以及多尺度目标回归的准确性;最后,通过使用归一化Wasserstein距离方法来度量损失,替代了原有的Intersection over Union度量,降低了小目标位置偏差的敏感性,增强了小目标的回归准确性。实验结果表明,改进模型在SIXray、CLCXray和OPIXray数据集上平均检测精度达到92.9%、76.2%和91.2%,相比原始算法分别提升了6.5%、2%和1.8%;所提出模型在轻量化的同时能够进一步提高检测能力,可以满足实时检测要求,具有较好的应用价值。
