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.

Deep Learning Based Automatic Crack Detection and Segmentation for Unmanned Aerial Vehicle Inspections Automatic crack detection and segmentation plays a significant role in the whole system of unmanned aerial vehicle inspections. In this paper, we have implemented a deep learning framework for crack detection based on classical network architectures including Alexnet, VGG and Resnet. Moreover, inspired by the feature pyramid network architecture, a hierarchical convolutional neural network (CNN) deep learning framework which is efficient in crack segmentation is also proposed, and the performance of it is compared with other state of the art network architecture. We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmarkdataset on the internet for crack detection and segmentation, which is opensourced for the research community. Our feature pyramid crack segmentation network is tested on the benchmarkdataset and gives satisfactory segmentation results. A framework for automatic unmanned aerial vehicle inspections is also proposed for the crack inspection tasks of various concrete structures.