Single-shot multibox detector- and building information modeling-based quality inspection model for construction projects

Guo, Ma; Wu, Ming; Wu, Zhijiang; Yang, Wenjing

doi:10.1016/j.jobe.2021.102216

Cited by 21 publications

(21 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another strategy for defect localization is to capture images at predefined positions with known coordinates. For example, Ma et al (2021) performed an indoor facility inspection with a ground robot, which is programmed to take photos at designated locations.…”

Section: Vision-based Defect Reconstructionmentioning

confidence: 99%

“…Li et al (2018) proposed a unified deep neural network that can simultaneously detect and identify the geolocations of concrete defects. Ma et al (2021) related concrete defects with their locations via a predefined data collection scheme. Although these studies can roughly indicate where the defects occur, they do not have sufficient granularity to characterize defect geometry and dimensions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic‐rich building information model

Chen

Lou

2022

Computer aided Civil Eng

View full text Add to dashboard Cite

Concrete defect information is of vital importance to building maintenance. Increasingly, computer vision has been explored for automated concrete defect detection. However, existing studies suffer from the challenging issue of false positives. In addition, 3D reconstruction of the defects to pinpoint their positions and geometries has not been sufficiently explored. To address these limitations, this study proposes a novel computational approach for detecting and reconstructing concrete defects from geotagged aerial images. A bundle registration algorithm is devised to align a batch of aerial photographs with a building information model (BIM). The registration enables the retrieval of material semantics in BIM to determine the regions of interest for defect detection. It helps rectify the camera poses of the aerial images, enabling precise defect reconstruction.Experiments demonstrate the effectiveness of the approach, which significantly reduced the false discovery rate from 70.8% to 56.8%, resulting in an intersection over union 6.4% higher than that of the traditional method. The geometry of the defects was successfully reconstructed in 3D world space. This study opens a new avenue to advance the field of defect detection by exploiting the rich information from BIM. The approach can be deployed at scale, supporting urban renovation, numerical simulation, and other smart applications.

show abstract

Section: Vision-based Defect Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic‐rich building information model

Chen

Lou

2022

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…In addition, Ma, Wu, Wu and Yang [37] adopted BIM to assist both the inspection data collection and the management process. The inspection surface was first divided into pieces in BIM to guide image acquisition, and pieces with defects detected in images were coloured for visualisation.…”

Section: Inspection Path Planning (1) Inspection Data Collection (1)mentioning

confidence: 99%

Integrating UAV and BIM for automated visual building inspection: a systematic review and conceptual framework

Zhang

Zou

Dimyadi

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Unmanned Aerial Vehicle (UAV) has gained popularity in visual building inspection due to its unique advantages of high mobility and low cost. However, it has been a challenge to efficiently manage the large number of images collected by the camera-equipped UAV for analysis and support building condition assessment. One potential solution is to manage the UAV data and building data through the platform of Building Information Modelling (BIM). However, little research has been found that successfully combined UAV and BIM for the visual inspection of buildings. This research explores the state-of-the-art UAV and BIM for visual building inspection through a systematic literature review. A mixed quantitative-qualitative analysis is conducted to provide insights into the application of UAV and BIM in visual building inspection based on identified academic publications (i.e., 48 articles on UAV, 31 articles on BIM, and 4 articles on integrating UAV and BIM). Furthermore, challenges and possible research opportunities are highlighted to guide future research: (1) integrating UAV and BIM to automate the visual building inspection process, including the data collection and data management; (2) considering the safety concern induced by the complex surrounding environment for the BIM-based UAV flight path planning; (3) developing an efficient way for managing UAV images in BIM. In addition, a conceptual framework for integrating UAV and BIM towards automated visual building inspection is proposed to serve as a roadmap.

show abstract

“…In addition, they used affinity propagation clustering to enhance the performance of SSD, offering an optimum balance between speed and accuracy. Similarly, Ma et al [23] proposed a quality inspection framework by combining an object detector (SSD) used to detect five different types of defects with building information modeling to improve productivity and overcome unnecessary deviations caused by human judgment. Zhong et al [24] proposed a deep learning-based text classification approach by integrating natural language processing and CNN for accident reports and utilized latent dirichlet allocation to understand the factors contributing to construction accidents.…”

mentioning

confidence: 99%

Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method

et al. 2022

View full text Add to dashboard Cite

According to the Center for Construction Research and Training (CPWR) and the Korea Occupational Safety & Health Agency (KOSHA), falls from ladders are a leading cause of fatalities. The current safety inspection process to enforce height-related rules is manual and time-consuming. It requires the physical presence of a safety manager, for whom it is sometimes impossible to monitor an entire area in which ladders are being used. Deep learning-based computer vision technology has the potential to capture a large amount of useful information from a digital image. Therefore, this paper presents a deep learning-based height assessment method using a single known value in an image to measure working height, monitor compliance to safety rules, and ensure worker safety. The proposed method comprises (1) extraction of safety rules from the KOSHA database related to the A-type ladder; (2) object detection (Single Shot Multibox Detector SSD) (3) a height-computing module (HCM) to estimate the working height of the worker (how high a worker is from the ground); and (4) classification of worker behavior (using the developed SSD-based HCM) based on the best practices derived from the KOSHA database. The developed algorithm has been tested on four different scenarios based on KOSHA safety rules, with heights ranging from under 1.2 m to over 2 m. Additionally, the proposed method was evaluated on 300 images for binary classification (safe and unsafe) and achieved an overall accuracy of 85.33%, verifying its feasibility for intelligent height estimation and compliance monitoring. INDEX TERMS Falls from ladders, vision intelligence-based monitoring, construction safety rules, visionbased height estimation, deep learning. I. INTRODUCTIONConstruction work is likely to expose workers to hazardous situations because of its distinctive, dynamic, and complex nature. Compared with the workers in other industries, those in the construction industry are more prone to occupational accidents that could potentially lead to fatalities [1]-[3]. The most common construction accidents include fall from height (FFH), collision with objects, electrocution, and being stuckThe associate editor coordinating the review of this manuscript and approving it for publication was Turgay Celik . in between machinery. Among these, FFH is the most frequent cause of accidents at construction sites. According to the U.S. Bureau of Labor Statistics, the number of fatalities due to falls to a lower level increased by 26% from 2011 to 2016, which includes ladders (836 fatal injuries) and rooftops (763). The most common height of a fall was reported as 30 ft (658 deaths) [4]. In addition, the Center for Construction Research and Training (CPWR) reports that ladder (93)-related accidents were a leading cause of fatal injuries from 2011 to 2019 [5]. Moreover, in South Korea, industrial accidents are increasing, with accidents at construction sites

show abstract

Single-shot multibox detector- and building information modeling-based quality inspection model for construction projects

Cited by 21 publications

References 43 publications

Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic‐rich building information model

Automatic concrete defect detection and reconstruction by aligning aerial images onto semantic‐rich building information model

Integrating UAV and BIM for automated visual building inspection: a systematic review and conceptual framework

Fall Prevention From Ladders Utilizing a Deep Learning-Based Height Assessment Method

Contact Info

Product

Resources

About