In this study,we explore the problem of hypothesis testing for white noise in high-dimensional settings,where the dimension of the random vector may exceed the sample sizes.We introduce a test procedure based on spati...In this study,we explore the problem of hypothesis testing for white noise in high-dimensional settings,where the dimension of the random vector may exceed the sample sizes.We introduce a test procedure based on spatial-sign for high-dimensional white noise testing.This new spatial-sign-based test statistic is designed to emulate the test statistic proposed by Paindaveine and Verdebout[(2016).On high-dimensional sign tests.Bernoulli,22(3),1745–1769.],but under a more generalized scatter matrix assumption.We establish the asymptotic null distribution and provide the asymptotic relative efficiency of our test in comparison with the test proposed by Feng et al.[(2022).Testing for high-dimensional white noise.arXiv:2211.02964.]under certain specific alternative hypotheses.Simulation studies further validate the efficiency and robustness of our test,particularly for heavy-tailed distributions.展开更多
目的随着自动驾驶和辅助驾驶的快速发展,交通标志识别研究变得越来越重要。但是现阶段交通标志识别算法对交通标志识别的精度较低,尤其在面对目标背景较为复杂、光照不足和小目标交通标志的场景时,更加容易出现错检和漏检情况。针对以...目的随着自动驾驶和辅助驾驶的快速发展,交通标志识别研究变得越来越重要。但是现阶段交通标志识别算法对交通标志识别的精度较低,尤其在面对目标背景较为复杂、光照不足和小目标交通标志的场景时,更加容易出现错检和漏检情况。针对以上问题,提出了一种改进YOLOv7(you only look once version 7)的交通标志识别模型。方法首先,采用空间金字塔池化快速跨级部分连接(spatial pyramid pooling fast cross stage partial concat,SPPFCSPC)方法,替换YOLOv7算法使用的空间金字塔池化跨级部分连接(spatial pyramid pooling cross stage partial concat,SPPCSPC)方法,提高算法的特征提取能力。其次,采用加权双向特征金字塔网络(bi-directional feature pyra⁃mid network,BiFPN),增强算法的多尺度特征融合能力。接着,采用一种新的框间距离度量的归一化Wasserstein距离(normalized Wasserstein distance,NWD)方法,解决传统的IoU(intersection over union)度量对小目标交通标志检测过于敏感的问题。最后,使用特征内容的感知重组(content-aware reassembly of feature,CARAFE)算子,通过输入的特征,自适应生成上采样内核,有效地增加模型的感受域,更好地利用目标周边的信息,减少交通标志错检和漏检情况。结果实验结果表明,在减少算法参数量的基础上,改进算法在TT100K交通标志数据集上的mAP@0.5和mAP@0.5∶0.9值分别达到了92.50%和72.21%,较原始的YOLOv7算法分别提高了3.24%和1.83%。同时,在具有小目标特性的CCTSDB交通标志数据集和整理的国外交通标志数据集上验证了模型改进的有效性。结论通过实验验证和主客观评价,证明了本文改进算法的可行性,能够有效地对多种环境下的小目标交通标志进行识别,并在降低算法参数量的前提下,进一步提高了YOLOv7算法对交通标志识别的平均精度。展开更多
基金supported by the National Natural Science Foundation of China(Grants 12101335 and 12271271)the Natural Science Foundation of Tianjin(Grant 21JCQNJC00020)+5 种基金the Fundamental Research Funds for the Central Universities,Nankai University(Grants 63211088,63221050,and 63231013)Wukong Investment Research Fundspartially supported by the China National Key R&D Programunder Grant Nos.2022YFA1003703,2022YFA1003800,and 2019YFC1908502the National Natural Science Foundation of China under Grant Nos.12226007,12271271,11925106,12231011,11931001 and 11971247the Fundamental Research Funds for the Central Universities under Grant No.ZB22000105Shenzhen Wukong Investment Management Co.Ltd.
文摘In this study,we explore the problem of hypothesis testing for white noise in high-dimensional settings,where the dimension of the random vector may exceed the sample sizes.We introduce a test procedure based on spatial-sign for high-dimensional white noise testing.This new spatial-sign-based test statistic is designed to emulate the test statistic proposed by Paindaveine and Verdebout[(2016).On high-dimensional sign tests.Bernoulli,22(3),1745–1769.],but under a more generalized scatter matrix assumption.We establish the asymptotic null distribution and provide the asymptotic relative efficiency of our test in comparison with the test proposed by Feng et al.[(2022).Testing for high-dimensional white noise.arXiv:2211.02964.]under certain specific alternative hypotheses.Simulation studies further validate the efficiency and robustness of our test,particularly for heavy-tailed distributions.
文摘目的随着自动驾驶和辅助驾驶的快速发展,交通标志识别研究变得越来越重要。但是现阶段交通标志识别算法对交通标志识别的精度较低,尤其在面对目标背景较为复杂、光照不足和小目标交通标志的场景时,更加容易出现错检和漏检情况。针对以上问题,提出了一种改进YOLOv7(you only look once version 7)的交通标志识别模型。方法首先,采用空间金字塔池化快速跨级部分连接(spatial pyramid pooling fast cross stage partial concat,SPPFCSPC)方法,替换YOLOv7算法使用的空间金字塔池化跨级部分连接(spatial pyramid pooling cross stage partial concat,SPPCSPC)方法,提高算法的特征提取能力。其次,采用加权双向特征金字塔网络(bi-directional feature pyra⁃mid network,BiFPN),增强算法的多尺度特征融合能力。接着,采用一种新的框间距离度量的归一化Wasserstein距离(normalized Wasserstein distance,NWD)方法,解决传统的IoU(intersection over union)度量对小目标交通标志检测过于敏感的问题。最后,使用特征内容的感知重组(content-aware reassembly of feature,CARAFE)算子,通过输入的特征,自适应生成上采样内核,有效地增加模型的感受域,更好地利用目标周边的信息,减少交通标志错检和漏检情况。结果实验结果表明,在减少算法参数量的基础上,改进算法在TT100K交通标志数据集上的mAP@0.5和mAP@0.5∶0.9值分别达到了92.50%和72.21%,较原始的YOLOv7算法分别提高了3.24%和1.83%。同时,在具有小目标特性的CCTSDB交通标志数据集和整理的国外交通标志数据集上验证了模型改进的有效性。结论通过实验验证和主客观评价,证明了本文改进算法的可行性,能够有效地对多种环境下的小目标交通标志进行识别,并在降低算法参数量的前提下,进一步提高了YOLOv7算法对交通标志识别的平均精度。
