Acoustic Inspection of Concrete Structures Using Active Weak Supervision and Visual Information Concrete structures are featured heavily in most modern societies. In recent years, the need to inspect those structures has been a growing concern and the automation of inspection methods is highly demanded. Acoustic methods such as the hammering test are one of the most popular nondestructive testing methods for this task. In this paper, an approach to defect detection in concrete structures with active weak supervision and visual information is proposed. Based on audio and position information, pairs of samples are actively queried to a user on their similarity. Those are used to transform the feature space into a favorable one, in a weakly supervised fashion, for clustering defect and nondefect samples, reinforced by position information. Experiments conducted in both laboratory conditions and in field conditions proved the effectiveness of the proposed method.

Deep Neural Network based Visual Inspection with 3D Metric Measurement of Concrete Defects using Wallclimbing Robot 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 wallclimbing robot. The system consists of four modules: robotics data collection module to obtain RGBD images and IMU measurement, visualinertial SLAM module to generate pose coupled keyframes with depth information, InspectionNet module to classify each pixel into three classes (background, 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 spatialcontextual visualization. The system enables the metric model of each surface flaw patch with pixellevel 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.