Earthwork productivity analysis is essential for successful construction projects.If productivity analysis results can be accessed anytime and anywhere,then project management can be performed more efficiently.To this...Earthwork productivity analysis is essential for successful construction projects.If productivity analysis results can be accessed anytime and anywhere,then project management can be performed more efficiently.To this end,this paper proposes an earthwork productivity monitoring framework via a real-time scene updating multi-vision platform.The framework consists of four main processes:1)site-optimized database development;2)real-time monitoring model updating;3)multi-vision productivity monitoring;and 4)web-based monitoring platform for Internetconnected devices.The experimental results demonstrated satisfactory performance,with an average macro F1-score of 87.3%for continuous site-specific monitoring,an average accuracy of 86.2%for activity recognition,and the successful operation of multi-vision productivity monitoring through a web-based platform in real time.The findings can contribute to supporting site managers to understand real-time earthmoving operations while achieving better construction project and information management.展开更多
为提高无人车障碍物检测跟踪的精度和稳定性,首先针对YOLO v5(You only look once version 5,YOLO v5)网络存在的语义信息和候选框信息丢失的问题,引入深度可分离空洞空间金字塔结构与目标框加权融合算法完成对网络的优化;其次针对单阶...为提高无人车障碍物检测跟踪的精度和稳定性,首先针对YOLO v5(You only look once version 5,YOLO v5)网络存在的语义信息和候选框信息丢失的问题,引入深度可分离空洞空间金字塔结构与目标框加权融合算法完成对网络的优化;其次针对单阶段障碍物点云聚类精度低的问题,设计一种考虑点云距离与外轮廓连续性的两阶段障碍物点云聚类方法并完成三维包围盒的建立;最后将注意力机制引入MobileNet使网络更加聚焦于目标对象特有的视觉特征,并综合利用视觉特征和三维点云信息共同构建关联性度量指标,提高匹配精度。利用KITTI数据集对构建的障碍物目标检测、跟踪与测速算法进行仿真测试,并搭建实车平台进行真实环境试验,验证所提算法的有效性和真实环境可迁移性。展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(Nos.RS-2023-00241758,2021R1A2C2003696,and RS-2024-00334513).
文摘Earthwork productivity analysis is essential for successful construction projects.If productivity analysis results can be accessed anytime and anywhere,then project management can be performed more efficiently.To this end,this paper proposes an earthwork productivity monitoring framework via a real-time scene updating multi-vision platform.The framework consists of four main processes:1)site-optimized database development;2)real-time monitoring model updating;3)multi-vision productivity monitoring;and 4)web-based monitoring platform for Internetconnected devices.The experimental results demonstrated satisfactory performance,with an average macro F1-score of 87.3%for continuous site-specific monitoring,an average accuracy of 86.2%for activity recognition,and the successful operation of multi-vision productivity monitoring through a web-based platform in real time.The findings can contribute to supporting site managers to understand real-time earthmoving operations while achieving better construction project and information management.
文摘为提高无人车障碍物检测跟踪的精度和稳定性,首先针对YOLO v5(You only look once version 5,YOLO v5)网络存在的语义信息和候选框信息丢失的问题,引入深度可分离空洞空间金字塔结构与目标框加权融合算法完成对网络的优化;其次针对单阶段障碍物点云聚类精度低的问题,设计一种考虑点云距离与外轮廓连续性的两阶段障碍物点云聚类方法并完成三维包围盒的建立;最后将注意力机制引入MobileNet使网络更加聚焦于目标对象特有的视觉特征,并综合利用视觉特征和三维点云信息共同构建关联性度量指标,提高匹配精度。利用KITTI数据集对构建的障碍物目标检测、跟踪与测速算法进行仿真测试,并搭建实车平台进行真实环境试验,验证所提算法的有效性和真实环境可迁移性。
