Deep Neural Network based Visual Inspection with 3D Metric Measurement of Concrete Defects using Wall-climbing Robot
文献类型：会议论文
| 作者 | Yang L(杨亮)1,3 ; Li, Bing4; Yang GY(杨国永)3; Chang Y(常勇)3; Liu ZM(刘钊铭)3; Jiang, Biao2
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| 出版日期 | 2019 |
| 会议日期 | November 3-8, 2019 |
| 会议地点 | Macau, China |
| 页码 | 2849-2854 |
| 英文摘要 | This paper presents a novel metric inspection robot system using a deep neural network to detect and measure surface flaws (i.e., crack and spalling) on concrete structures performed by a wall-climbing robot. The system consists of four modules: robotics data collection module to obtain RGB-D images and IMU measurement, visual-inertial SLAM module to generate pose coupled key-frames with depth information, InspectionNet module to classify each pixel into three classes (back-ground, crack and spalling), and 3D registration and map fusion module to register the flaw patch into registered 3D model overlaid and highlighted with detected flaws for spatial-contextual visualization. The system enables the metric model of each surface flaw patch with pixel-level accuracy and determines its location in 3D space that is significant for structural health assessment and monitoring. The InspectionNet achieves an average accuracy of 87.64% for crack and spalling inspection. We also demonstrate our InspectionNet is robust to view angle, scale and illumination variation. Finally, we design a metric voxel volume map to highlight the flaw in 3D model and provide location and metric information. |
| 产权排序 | 1 |
| 会议录 | 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
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| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2153-0858 |
| ISBN号 | 978-1-7281-4004-9 |
| WOS记录号 | WOS:000544658402062 |
| 源URL | [http://ir.sia.cn/handle/173321/26424]  |
| 专题 | 工艺装备与智能机器人研究室 |
| 作者单位 | 1.Shenyang Institute of Automation, CCNY Robotics Lab, New York, United States 2.Hostos Community College, Department of Natural Sciences, New York, NY, United States 3.University of Chinese Academy of Sciences, Shenyang Institute of Automation, Chinese Academy of Sciences, China 4.Clemson University, Department of Automotive Engineering, South Carolina, United States |
| 推荐引用方式 GB/T 7714 | Yang L,Li, Bing,Yang GY,et al. Deep Neural Network based Visual Inspection with 3D Metric Measurement of Concrete Defects using Wall-climbing Robot[C]. 见:. Macau, China. November 3-8, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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