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Enclosed spaces are common in built structures but pose a challenge to many forms of manual or robotic surveying and maintenance tasks. Part of this challenge is to train robot systems to understand their environment without human intervention. This paper presents a method to automatically classify features within a closed void using deep learning. Specifically, the paper considers a robot placed under floorboards for the purpose of autonomously surveying the underfloor void. The robot uses images captured using an RGB camera to identify regions such as floorboards, joists, air vents and pipework. The paper first presents a standard mask regions convolutional neural network approach, which gives modest performance. The method is then enhanced using a two-stage transfer learning approach with an existing dataset for interior scenes. The conclusion from this work is that, even with limited training data, it is possible to automatically detect many common features of such areas. • A method for semantic segmentation in closed spaces • Database of underfloor images are captured and segmented using a mask R-CNN. • Two-stage transfer learning and other optimisations presented to maximise accuracy • Case-study application to the detection of floorboards, joists, vents, pipes, etc for automated insulation spraying • Mapping of segmented data labels to 3D point cloud data [ABSTRACT FROM AUTHOR]

Manual processing of Ground Penetrating Radar (GPR) images is a very time-intensive task. The authors proposed a novel computer vision-based method for automatic detection of rebars in complex GPR images in highly deteriorated concrete bridge decks. The proposed detection model consists of a fine-tuned Histogram of Oriented Gradients feature descriptor, a Multi-Layer Perceptron for classification, and a post processing algorithm for eliminating false detections and labeling rebar in Region of Interest. State-of-art results are obtained on testing the method on real bridge deck GPR data and comparing the results with RADAN software. Overall accuracy of 89.4% is obtained on URIGPRv1.0 dataset, which is introduced in this paper. The proposed method is 54.35% more accurate comparing to the results obtained by RADAN software. The proposed classifier outperformed accuracy of a 3-layer convolutional neural network by 11.9%. • Proposing a novel detection chain yields a new state-of-the-art result in rebar detection in GPR B-scan images • Optimizing HOG/LBPH feature computation and MLP model parameters for maximizing F-Measure in classification task • Developing a post-processing algorithm for minimizing false RoIs, removing non-top layer RoI, and extracting coordinates • Introducing a GPR dataset with 8,000 records, named URIGPRv1.0, cropped from real concrete bridge deck GPR B-scan images. • A computer program, named URICAB, is developed for automating interpretation of GPR data [ABSTRACT FROM AUTHOR]