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.

With the increasing interest in mobile robots for construction applications, autonomous navigation in unstructured and uneven construction sites has become a critical challenge. To ensure the safe and robust navigation of a robot in such environments, this study develops an optimal obstacle-avoiding path planner for stable posture (OOPS) that minimizes the distance to the goal area and stabilizes the posture of the robot. In this study, a new algorithm was developed by significantly improving the quick-rapidly exploring random tree* (Q-RRT*) algorithm to find a feasible path and converge to the optimal path more quickly. A cost function was defined to stabilize the posture of the robot. A simulation experiment was conducted to examine the feasibility of the OOPS algorithm, and its performance was compared with those of other algorithms. The OOPS algorithm was also validated in an experiment in a real-world outdoor environment with sloped hills and several obstacles. The results demonstrate that the OOPS algorithm outperforms other algorithms in terms of the time needed to find the initial solution, time to convergence on the optimal solution, and rate of success in reaching the goal. A mobile robot with the OOPS algorithm is able to start navigating sooner, and the algorithm takes less time than other algorithms to produce paths that are closer to the optimal path. The robot’s posture is more stable when it follows the path obtained from the OOPS algorithm in both simulation and real-world tests. Therefore, the OOPS algorithm can be effectively used for applications in uneven, highly sloped, and unstructured outdoor environments.

Because point clouds have reality measurements of physical objects, they are often used to reconstruct the as-built three-dimensional (3D) model of building construction sites through a modeling process called Scan-to-building information models (BIM). However, the reality measurements in point cloud data, such as actual color and deformed shapes of the original objects, could disappear during the solid modeling process. In addition, the conventional Scan-to-BIM pipeline still requires significant time and manual effort for object classification and shape representation. To address these problems, this study proposes a novel information modeling framework for point clouds, called point cloud information modeling (PCIM). PCIM can automatically recognize construction objects and their properties with deep learning approaches. Furthermore, it can store information in the original point cloud data with a hierarchical structure, rather than converting it to a solid or rigid model. To validate the overall PCIM concept, this research conducted a case study with an actual building construction project. The test results demonstrate that PCIM can be an effective tool for the as-is information modeling of structures and facilities during construction.

The construction industry has been trying to enhance the level of digitalization and autonomy by adopting various communication and information technologies (ICT), e.g., augmented reality (AR), virtual reality (VR), robotics, drones, or building information modeling (BIM). However, improvement of the safety and productivity in their domains is still a struggle. One of the main reasons for failing to accelerate their digital transformation is ignoring the deep understanding of the concept of digital twin, its usage, and the potential benefits of digital twins in the construction industry. Therefore, this paper investigated the impacts and potentials of digital twins on the construction industry through a quantitative systematic review assisted by the text mining method. The study presented the potential usability of digital twins, leading and core technologies, and applications, revealing their benefits and potential for optimizing project planning, execution, and management process. Through this comprehensive literature review, this study elucidated the distinctive features, advantages, and immense potential that digital twins bring to the construction field. The findings highlight the transformative impact of digital twins, providing critical insights for their broader adoption and groundbreaking applications in the industry. By addressing the challenges of adopting this technology, the article provided valuable insights for advancing research and the broad implementation of digital twins in the sector.

The primary advantages of utilizing a mobile robot, unmanned ground vehicle (UGV), are its autonomous navigation and data collection capabilities from a large-scale environment. However, the UGV is restricted by its limited perception ability in a cluttered environment. To address this limitation, an unmanned aerial vehicle (UAV) can cooperate with the UGV and build out a detailed and complete map. The objectives of this study are to operate aerial and ground robots cooperatively and to fuse point cloud data complementarily. This paper introduces an autonomous cooperation framework using UGV and UAV for 3D geometric data collection in a dynamic cluttered environment. First, UAV is deployed and collects 3D terrain data by using photo images to get an overall idea of the target site. Then, the path planning and stationary scanning locations for UGV are estimated using the gradient-based 3D model generated by UAV. This method was tested on a real-world construction site and obtained promising results. The proposed UAV-UGV cooperative robotic approach is expected to reduce human intervention in data collection and data processing time significantly. Furthermore, it enables frequent monitoring, updating, and analyzing a cluttered environment for timely decision making.