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作 者:廉继红[1] 薛维哥 王延年[1] 张楠[1] LIAN Jihong;XUE Weige;WANG Yannian;ZHANG Nan(School of Electronics and Information,Xi’an Polytechnic University,Xi’an 710048,China)
机构地区:[1]西安工程大学电子信息学院,陕西西安710048
出 处:《西安工程大学学报》2025年第2期1-9,共9页Journal of Xi’an Polytechnic University
基 金:陕西省科技厅一般项目(2022GY-053)。
摘 要:为了进一步确保自动驾驶的行人安全,针对人体姿态估计存在关键点误检、漏检、冗余的问题,以HRNet为骨干网络进行算法优化,从而进一步提高模型检测精度。首先,在图像特征提取时设计了一个人体姿态估计模型推理网络RSGNet,在关键点推理的过程中剔除干扰关键点带来的影响,提高模型对关键点信息的有效利用;其次,针对自遮挡或者外界干扰的影响导致图像细节信息不完全的问题,在图像特征处理时加入了卷积注意力模块(convolutional block attention module,CBAM),该模块结合了空间与通道的关联融合信息,减少了前景、背景等信息对图像处理的负面影响。实验结果表明:相较于基准模型HRNet方法,改进后的网络模型明显提高了人体姿态估计的检测精度,在公共数据集COCO的平均准确率(average precision,AP)提高了7.3%,在公共数据集MPII的AP提高了3.0%。In order to further ensure pedestrian safety in autonomous driving,this article addresses the issues of key point false detection,missed detection,and redundancy in human pose estimation.HRNet was used as the backbone network for algorithm optimization to further improve model detection accuracy.Firstly,a human pose estimation model inference network RSGNet was designed for image feature extraction,which eliminates the influence of interfering keypoints during the keypoint inference process and improves the effective utilization of keypoint information by the model.Secondly,in response to the problem of incomplete image detail information caused by self occlusion or external interference,a convolutional block attention module(CBAM)was added to image feature processing.This module combines spatial and channel correlation fusion information to reduce the negative impact of foreground,background,and other information on image processing.The experimental results show that compared with the benchmark model HRNet method,the improved network model significantly improves the detection accuracy of human pose estimation,with an average precision(AP)increase of 7.3%in the public dataset COCO,and the AP in the public data set MPII is increased by 3.0%.
关 键 词:姿态估计 自动驾驶 关键点 注意力机制 空间注意力 通道注意力
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]
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