人体姿态估计是计算机视觉的基础性算法之一,为了探究人体姿态估计领域的研究发展趋势,文章首先介绍了基于卷积的经典人体姿态估计算法,论述各算法的基本原理及算法改进,其次对最新的基于自注意力模型(Transformer)的算法进行梳理,最后...人体姿态估计是计算机视觉的基础性算法之一,为了探究人体姿态估计领域的研究发展趋势,文章首先介绍了基于卷积的经典人体姿态估计算法,论述各算法的基本原理及算法改进,其次对最新的基于自注意力模型(Transformer)的算法进行梳理,最后介绍了常用的公开数据集和模型评价指标,选取了几个经典算法进行对比分析,平均精度在马克斯·普朗克信息研究所(Max Planck Institute Informatik,MPII)数据集达到80%以上,在微软公共对象上下文(Common Objects in Context,COCO)数据集达到60%以上,得到卷积结构和Transformer结构互有优劣的结论。展开更多
Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The m...Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.展开更多
文摘人体姿态估计是计算机视觉的基础性算法之一,为了探究人体姿态估计领域的研究发展趋势,文章首先介绍了基于卷积的经典人体姿态估计算法,论述各算法的基本原理及算法改进,其次对最新的基于自注意力模型(Transformer)的算法进行梳理,最后介绍了常用的公开数据集和模型评价指标,选取了几个经典算法进行对比分析,平均精度在马克斯·普朗克信息研究所(Max Planck Institute Informatik,MPII)数据集达到80%以上,在微软公共对象上下文(Common Objects in Context,COCO)数据集达到60%以上,得到卷积结构和Transformer结构互有优劣的结论。
文摘Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.
