The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete s...The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects.Secondly, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research community.Finally, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.展开更多
为解决35 k V交流输电线路直线塔巡检无人机自适应能力差,导致巡检作业易出错中断的问题,该文设计一种新型无人机巡检自动化控制系统。此系统由MPU9250九自由度惯性传感器、加速度计、磁力计,采集输电线路直线塔无人机巡检状态数据,通...为解决35 k V交流输电线路直线塔巡检无人机自适应能力差,导致巡检作业易出错中断的问题,该文设计一种新型无人机巡检自动化控制系统。此系统由MPU9250九自由度惯性传感器、加速度计、磁力计,采集输电线路直线塔无人机巡检状态数据,通过积分法、三角函数关系、倾斜补偿方法,解算获取直线塔无人机巡检的姿态、速度、位置数据信息;使用基于模糊PID的自动化控制器,计算无人机巡检的姿态、速度、位置偏差与偏差率,自动调整无人机旋翼转速,控制无人机巡检状态,完成35 kV交流输电线路直线塔无人机巡检自动化控制。实验中,此系统使用下,无人机巡检状态与指定轨迹一致,俯仰角、横滚角、航向角偏差值为0°。展开更多
基金supported in part by the U.S.National Science Foundation(IIP-1915721)the U.S.Department of TransportationOffice of the Assistant Secretary for Research and Technology(USDOTOST-R)(69A3551747126)through INSPIRE University Transportation Center(http//inspire-utc.mst.edu)at Missouri University of Science and Technology。
文摘The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects.Secondly, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research community.Finally, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.
文摘为解决35 k V交流输电线路直线塔巡检无人机自适应能力差,导致巡检作业易出错中断的问题,该文设计一种新型无人机巡检自动化控制系统。此系统由MPU9250九自由度惯性传感器、加速度计、磁力计,采集输电线路直线塔无人机巡检状态数据,通过积分法、三角函数关系、倾斜补偿方法,解算获取直线塔无人机巡检的姿态、速度、位置数据信息;使用基于模糊PID的自动化控制器,计算无人机巡检的姿态、速度、位置偏差与偏差率,自动调整无人机旋翼转速,控制无人机巡检状态,完成35 kV交流输电线路直线塔无人机巡检自动化控制。实验中,此系统使用下,无人机巡检状态与指定轨迹一致,俯仰角、横滚角、航向角偏差值为0°。
