scholarly journals Unmanned Aerial Vehicles (UAVs) for Physical Progress Monitoring of Construction

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4227
Author(s):  
Nicolás Jacob-Loyola ◽  
Felipe Muñoz-La Rivera ◽  
Rodrigo F. Herrera ◽  
Edison Atencio
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Progress Monitoring ◽  
Residential Building ◽  
Point Clouds ◽  
Construction Site ◽  
Manual Operation ◽  
Work Site ◽  
Aerial Vehicles ◽  
Made In

The physical progress of a construction project is monitored by an inspector responsible for verifying and backing up progress information, usually through site photography. Progress monitoring has improved, thanks to advances in image acquisition, computer vision, and the development of unmanned aerial vehicles (UAVs). However, no comprehensive and simple methodology exists to guide practitioners and facilitate the use of these methods. This research provides recommendations for the periodic recording of the physical progress of a construction site through the manual operation of UAVs and the use of point clouds obtained under photogrammetric techniques. The programmed progress is then compared with the actual progress made in a 4D BIM environment. This methodology was applied in the construction of a reinforced concrete residential building. The results showed the methodology is effective for UAV operation in the work site and the use of the photogrammetric visual records for the monitoring of the physical progress and the communication of the work performed to the project stakeholders.

USE OF UNMANNED AERIAL VEHICLES AND COMPUTER VISION IN CONSTRUCTION SAFETY INSPECTIONS

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
Mohamad Abbas ◽  
Bahaa Eddine Mneymneh ◽  
Hiam Khoury
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Health And Safety ◽  
Automated System ◽  
Construction Site ◽  
Safety Regulations ◽  
Aerial Vehicles ◽  
Inspection Process ◽  
Processing Techniques ◽  
Safety Inspections

Construction is unarguably one of the most dangerous industries whereby many hazardous tasks and conditions exist, which may pose injuries, risks and fatalities to the workers. Hence, safety inspections are continuously carried out to maintain a safe environment. These typically involve safety officers who circulate around the construction site to detect unsafe working conditions, and ensure compliance with health and safety regulations. However, the task of efficiently supervising a large number of workers and consistently identifying all possible violations is still considered manual and tedious. Therefore, this paper takes the initial steps and presents work targeted at automating the safety inspection process using Unmanned Aerial Vehicles (UAVs). These are commonly known as drones and are small, aerial camera-equipped robots capable of rapidly visualizing spacious environments. More specifically, in this study, a UAV system is used to capture real-time videos from a construction site. The videos are then streamed to a central automated system and analyzed using digital image processing techniques to check whether construction workers are wearing personal protective equipment (PPE), in particular hard hats. The components of the proposed system were created and preliminary results highlighted the potential of using camera-equipped UAVs and computer vision to automate safety inspections in construction environments.

scholarly journals Efficient Reactive Obstacle Avoidance Using Spirals for Escape

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 51
Author(s):  
Fábio Azevedo ◽  
Jaime S. Cardoso ◽  
André Ferreira ◽  
Tiago Fernandes ◽  
Miguel Moreira ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Mobile Robotics ◽  
Point Clouds ◽  
Depth Camera ◽  
Main Task ◽  
Robust Method ◽  
Aerial Vehicles ◽  
Correct Execution ◽  
Low Altitude ◽  
High Degree

The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies.

scholarly journals Digitalización automática del patrimonio arqueológico a partir de fotogrametría

2015 ◽  
Vol 4 (8) ◽  
pp. 46 ◽  
Author(s):  
Pedro Ortiz Coder
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Laser Scanner ◽  
Archaeological Site ◽  
New Techniques ◽  
Aerial Vehicles ◽  
Photogrammetric Method

<p>New techniques in graphical heritage documentation have been improving recently. Modern photogrammetry and laser scanner constitute techniques with a good quality for those purposes. In this document, we will explain an easy photogrammetric method which permits to obtain accurate results. It is important to separate it from other methods based on computer vision with less accuracy. 4e photogrammetry solution is applied in this test through pictures taken from UAV (Unmanned Aerial Vehicles) and used on an archaeological site in Extremadura.</p>

scholarly journals Computer Vision in Autonomous Unmanned Aerial Vehicles—A Systematic Mapping Study

2019 ◽  
Vol 9 (15) ◽  
pp. 3196 ◽  
Author(s):  
Lidia María Belmonte ◽  
Rafael Morales ◽  
Antonio Fernández-Caballero
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Flight Control ◽  
Systematic Mapping Study ◽  
General View ◽  
Mapping Study ◽  
Vision Systems ◽  
Systematic Mapping ◽  
Aerial Vehicles ◽  
Assistant Robots

Personal assistant robots provide novel technological solutions in order to monitor people’s activities, helping them in their daily lives. In this sense, unmanned aerial vehicles (UAVs) can also bring forward a present and future model of assistant robots. To develop aerial assistants, it is necessary to address the issue of autonomous navigation based on visual cues. Indeed, navigating autonomously is still a challenge in which computer vision technologies tend to play an outstanding role. Thus, the design of vision systems and algorithms for autonomous UAV navigation and flight control has become a prominent research field in the last few years. In this paper, a systematic mapping study is carried out in order to obtain a general view of this subject. The study provides an extensive analysis of papers that address computer vision as regards the following autonomous UAV vision-based tasks: (1) navigation, (2) control, (3) tracking or guidance, and (4) sense-and-avoid. The works considered in the mapping study—a total of 144 papers from an initial set of 2081—have been classified under the four categories above. Moreover, type of UAV, features of the vision systems employed and validation procedures are also analyzed. The results obtained make it possible to draw conclusions about the research focuses, which UAV platforms are mostly used in each category, which vision systems are most frequently employed, and which types of tests are usually performed to validate the proposed solutions. The results of this systematic mapping study demonstrate the scientific community’s growing interest in the development of vision-based solutions for autonomous UAVs. Moreover, they will make it possible to study the feasibility and characteristics of future UAVs taking the role of personal assistants.

scholarly journals Survey of computer vision algorithms and applications for unmanned aerial vehicles

2018 ◽  
Vol 92 ◽  
pp. 447-463 ◽  
Author(s):  
Abdulla Al-Kaff ◽  
David Martín ◽  
Fernando García ◽  
Arturo de la Escalera ◽  
José María Armingol
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles

scholarly journals Autonomous trajectory following for an UAV based on computer vision

2020 ◽  
Vol 14 (1) ◽  
pp. 51-56
Author(s):  
Jorge Daniel Gallo Sanabria ◽  
Paula Andrea Mozuca Tamayo ◽  
Rafael Iván Rincón Fonseca
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Simple Algorithm ◽  
Aerial Vehicles ◽  
Package Delivery ◽  
Trajectory Following ◽  
The Way

The trajectory following performed by unmanned aerial vehicles has several advantages that can be taken to several applications, going from package delivery to agriculture. However, this involves several challenges depending on the way the following is performed, particularly in the case of trajectory following by using computer vision. In here we will show the design, the simulation and the implementation of a simple algorithm for trajectory following by using computer vision, this algorithm will be executed on a drone that will arrive into a wished point.

Improving progress monitoring by fusing point clouds, semantic data and computer vision

2020 ◽  
Vol 116 ◽  
pp. 103210
Author(s):  
Alex Braun ◽  
Sebastian Tuttas ◽  
André Borrmann ◽  
Uwe Stilla
Keyword(s):  
Computer Vision ◽  
Progress Monitoring ◽  
Point Clouds ◽  
Semantic Data

Forest fire flame and smoke detection from UAV-captured images using fire-specific color features and multi-color space local binary pattern

2020 ◽  
Vol 8 (4) ◽  
pp. 285-309
Author(s):  
F.M. Anim Hossain ◽  
Youmin M. Zhang ◽  
Masuda Akter Tonima
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Forest Fire ◽  
Local Binary Pattern ◽  
Color Space ◽  
Fire Detection ◽  
Large Field ◽  
Aerial Vehicles ◽  
Aerial Platforms ◽  
Forest Fire Detection

In recent years, the frequency and severity of forest fire occurrence have increased, compelling the research communities to actively search for early forest fire detection and suppression methods. Remote sensing using computer vision techniques can provide early detection from a large field of view along with providing additional information such as location and severity of the fire. Over the last few years, the feasibility of forest fire detection by combining computer vision and aerial platforms such as manned and unmanned aerial vehicles, especially low cost and small-size unmanned aerial vehicles, have been experimented with and have shown promise by providing detection, geolocation, and fire characteristic information. This paper adds to the existing research by proposing a novel method of detecting forest fire using color and multi-color space local binary pattern of both flame and smoke signatures and a single artificial neural network. The training and evaluation images in this paper have been mostly obtained from aerial platforms with challenging circumstances such as minuscule flame pixels, varying illumination and range, complex backgrounds, occluded flame and smoke regions, and smoke blending into the background. The proposed method has achieved F1 scores of 0.84 for flame and 0.90 for smoke while maintaining a processing speed of 19 frames per second. It has outperformed support vector machine, random forest, Bayesian classifiers and YOLOv3, and has demonstrated the capability of detecting challenging flame and smoke regions of a wide range of sizes, colors, textures, and opacity.

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