Detecting and Classifying Cranes Using Camera-Equipped UAVs for Monitoring Crane-Related Safety Hazards

Roberts, Dominic; Golparvar-Fard, Mani

doi:10.1061/9780784480847.055

Cited by 27 publications

(12 citation statements)

References 9 publications

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“…Safety is always a significant topic in the AEC industry (Nath et al 2020). Current research focus on various fields (Fang et al 2020): failure of wearing Personal protective equipment (PPE) (Fang et al 2018;Wu et al 2019), unsafe behavior (Ding et al 2018;Fang et al 2019), and exposure to hazardous area (Roberts et al 2017;Zhang et al 2019). Integrating with computer vision and AI technology, vision-based approaches have become more popular (Nath et al 2020).…”

Section: Safety Managementmentioning

confidence: 99%

See 1 more Smart Citation

Building a Next Generation AI Platform for AEC: A Review and Research Challenges

Wang¹,

He²,

Yu³

et al. 2020

CIB W78 Proceedings

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Transformation of the AEC industry has been found to face significant and persistent hurdles both internal and external to the industry, such as industrial fragmentation, difficulty in talent recruiting, inadequate collaboration and knowledge transfer. Meanwhile, advances and maturity in digital technologies recent years offer tremendous potential to tackle these challenges. Examples include Artificial Intelligence (AI) and deep learning, computer vision, big data analytics, Building Information Models (BIM), 3D scanning, Augmented Reality (AR), Unmanned Aerial Vehicles (UAVs), autonomous driving, and blockchain. We envision that the next generation platform for the AEC industry shall integrate this diverse list of digital technologies. In this paper, we review the stateof-the-art in related literature aiming at creating such an integrated platform. Our review is organized into the following four themes, and for each of them, we also identify gaps and challenges. 1. AI-based BIM model generation: this topic addresses how to make BIM models more ubiquitous, which includes the automatic generation of As-planned BIM model from 2D drawings and the automatic generation of As-built BIM model from site images/videos and point clouds. 2. Blockchain-based collaboration: this theme explores the possibilities of applying Blockchain for AEC to improve various essential aspects such as trust in multi-party collaboration and contract enforcement. 3. AR-based visualization: this topic studies the AR-based visualization for construction management, which aims to increase the efficiency of information transmission. 4. AI-based Construction management: this area reviews the application of AI in progress monitoring, safety management, quality management, and contract management. Based on the above analysis, we conclude by outlining potential topics of a research framework for creating a next generation AI platform for AEC.

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Section: Safety Managementmentioning

confidence: 99%

“…As a result, the average precision and recall rates were around 90% and 91% respectively. Concerning vision-based approaches, in Roberts et al (2017), CNN was employed to identify crane locations in real-time for the avoidance of safety hazards to their surroundings on construction sites.…”

Section: Safety Managementmentioning

confidence: 99%

Building a Next Generation AI Platform for AEC: A Review and Research Challenges

Wang¹,

He²,

Yu³

et al. 2020

CIB W78 Proceedings

View full text Add to dashboard Cite

show abstract

“…Similarly, Luo Hanbin improved convolutional neural network (CNN) that integrates Red–Green–Blue (RGB), optical flow, and gray stream CNNs, and the improved CNN could detect the worker’s activity to assist the project for personnel management [29]. Moreover, Dominic Roberts combined Unmanned Aerial Vehicle photography and image recognition technology to track on-site cranes and estimate 3D crane pose [30]. All of above-mentioned studies have provided more advanced technology for on-site safety management.…”

Section: Literature Reviewmentioning

confidence: 99%

Safety Distance Identification for Crane Drivers Based on Mask R-CNN

Yang

Yuan

Zhang

et al. 2019

Sensors

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Tower cranes are the most commonly used large-scale equipment on construction site. Because workers can’t always pay attention to the environment at the top of the head, it is often difficult to avoid accidents when heavy objects fall. Therefore, safety construction accidents such as struck-by often occurs. In order to address crane issue, this research recorded video data by a tower crane camera, labeled the pictures, and operated image recognition with the MASK R-CNN method. Furthermore, The RGB color extraction was performed on the identified mask layer to obtain the pixel coordinates of workers and dangerous zone. At last, we used the pixel and actual distance conversion method to measure the safety distance. The contribution of this research to safety problem area is twofold: On one hand, without affecting the normal behavior of workers, an automatic collection, analysis, and early-warning system was established. On the other hand, the proposed automatic inspection system can help improve the safety operation of tower crane drivers.

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“…Researchers have assessed the feasibility of UAV-enabled inspections as an active part of construction safety control (de Melo et al 2017). Vision-based techniques were employed on UAV-captured images to locate cranes (Roberts et al 2017), helping to reduce their safety hazards on jobsites. The quality of UAVcaptured visual data was observed to be a primary factor for UAV use in safety monitoring systems (Kim et al 2016); this further emphasizes the need for model-driven and automatically planned robotic inspections.…”

Section: Related Workmentioning

confidence: 99%

UAV Mission Planning Using Swarm Intelligence and 4D BIMs in Support of Vision-Based Construction Progress Monitoring and As-Built Modeling

Hamledari

Davari

Sajedi

et al. 2018

Construction Research Congress 2018

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Inspection planning is a primary element of computer vision-and unoccupied aerial vehicle (UAV)-enabled construction monitoring. Prior to the on-site deployment of camera-mounted UAVs, the inspection objectives need to be identified, and optimal inspection plans must be developed; Such plans should ensure complete data acquisition and minimize the use of UAV's limited flight time. The image capture configuration must be taken into account since it directly affects the downstream applications of the captured data such as progress detection and as-built modeling. This paper proposes a framework and a novel technique which utilizes four-dimensional (4D) building information models (BIM) and swarm intelligence to automatically generate the UAV inspection mission plans. It computationally supports both static and dynamic site layouts. The inspection objectives, their geometry, and their semantics are automatically extracted from BIM, and the corresponding elements are identified. An optimal inspection plan is developed using artificial intelligence, ensuring complete coverage of inspection targets while minimizing flight duration. The method has been tested in UAV-enabled data acquisition scenarios. It is based on the industry foundation classes (IFC), facilitating OpenBIM and reducing the costs associated with the lack of interoperability, a core challenge in information modeling. Due to the target extraction at element and sub-element levels, it supports computer vision-based construction progress monitoring and automated as-built and as-is BIM development.

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Detecting and Classifying Cranes Using Camera-Equipped UAVs for Monitoring Crane-Related Safety Hazards

Cited by 27 publications

References 9 publications

Building a Next Generation AI Platform for AEC: A Review and Research Challenges

Building a Next Generation AI Platform for AEC: A Review and Research Challenges

Safety Distance Identification for Crane Drivers Based on Mask R-CNN

UAV Mission Planning Using Swarm Intelligence and 4D BIMs in Support of Vision-Based Construction Progress Monitoring and As-Built Modeling

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