Construction progress estimation to ensure high productivity and quality is an essential component of the daily construction cycle. However, using three-dimensional (3D) laser-scanned point clouds for the purpose of measuring deviations between as-built structures and as-planned building information models (BIMs) remains cumbersome due to difficulties in data registration, segmentation, annotation, and modeling in large-scale point clouds. This research proposes the use of a data-driven deep learning framework to automatically detect and classify building elements from a laser-scanned point cloud scene. The point cloud is first converted into a graph representation, in which vertices represent points and edges represent connections between points within a fixed distance. An edge-based classifier is used to discard edges connecting points from different objects and to form connected components from points in the same object. Next, a point-based object classifier is used to determine the type of building component based on the segmented points and augmented with context from surrounding points. Finally, each detected object is matched with a corresponding BIM entity based on the nearest neighbor in the feature space.

Night-time surveillance is important for safety and security purposes. For this reason, several studies have attempted to automatically detect people intruding into restricted areas by using infrared cameras. However, detecting people from infrared CCTV (closed-circuit television) is challenging because they are usually installed in overhead locations and people only occupy small regions in the resulting image. Therefore, this study proposes an accurate and efficient method for detecting people in infrared CCTV images during the night-time. For this purpose, three different infrared image datasets were constructed; two obtained from an infrared CCTV installed on a public beach and another obtained from a forward looking infrared (FLIR) camera installed on a pedestrian bridge. Moreover, a convolution neural network (CNN)-based pixel-wise classifier for fine-grained person detection was implemented. The detection performance of the proposed method was compared against five conventional detection methods. The results demonstrate that the proposed CNN-based human detection approach outperforms conventional detection approaches in all datasets. Especially, the proposed method maintained F1 scores of above 80% in object-level detection for all datasets. By improving the performance of human detection from infrared images, we expect that this research will contribute to the safety and security of public areas during night-time.

Robotics and Artificial Intelligence (AI) have been inextricably intertwined since their inception. Today, AI-Robotics systems have become an integral part of our daily lives, from robotic vacuum cleaners to semi-autonomous cars. These systems are built upon three fundamental architectural elements: perception, navigation and planning , and control . However, while the integration of AI in Robotics systems has enhanced the quality of our lives, it has also presented a serious problem - these systems are vulnerable to security attacks. The physical components, algorithms, and data that makeup AI-Robotics systems can be exploited by malicious actors, potentially leading to dire consequences. Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions: attack surfaces, ethical and legal concerns , and Human-Robot Interaction (HRI) security . Our goal is to provide readers, developers and other stakeholders with a holistic understanding of these areas to enhance the overall AI-Robotics system security. We begin by identifying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems. Besides, emerging trends such as HRI are discussed, considering privacy, integrity, safety, trustworthiness, and explainability concerns. Finally, we present our vision for future research directions in this dynamic and promising field.