A Comparative Study of Machine Learning Classification for Color-based Safety Vest Detection on Construction-Site Images

Seong, Hyeonwoo; Son, Hyojoo; Kim, Chang-Wan

doi:10.1007/s12205-017-1730-3

Cited by 31 publications

(19 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The detection methods for PPE are diversified, and suitable methods can be selected for the detection of the salient features of protective equipment (e.g., shape, color). Common detection methods include HOG feature detection [16], color-based feature extraction, circular Huffman transform (CHT) [43] and HSV color detection [44]. By matching the detected human body with PPE, it can help to make the judgement whether a worker is wearing PPE correctly or not.…”

Section: Use Of Personal Protective Equipmentmentioning

confidence: 99%

Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

Liu

Meng

et al. 2021

Buildings

View full text Add to dashboard Cite

The unsafe behavior of construction workers is one of the main causes of safety accidents at construction sites. To reduce the incidence of construction accidents and improve the safety performance of construction projects, there is a need to identify risky factors by monitoring the behavior of construction workers. Computer vision (CV) technology, which is a powerful and automated tool used for extracting images and video information from construction sites, has been recognized and adopted as an effective construction site monitoring technology for the identification of risky factors resulting from the unsafe behavior of construction workers. In this article, we introduce the research background of this field and conduct a systematic statistical analysis of the relevant literature in this field through the bibliometric analysis method. Thereafter, we adopt a content-based analysis method to depict the historical explorations in the field. On this basis, the limitations and challenges in this field are identified, and future research directions are proposed. It is found that CV technology can effectively monitor the unsafe behaviors of construction workers. The research findings can enhance people’s understanding of construction safety management.

show abstract

Section: Use Of Personal Protective Equipmentmentioning

confidence: 99%

Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

Liu

Meng

et al. 2021

Buildings

View full text Add to dashboard Cite

show abstract

“…Recently, relatively few studies have been conducted for the detection of masks, safety vests, and gloves. Seong et al [ 24 ] used different approaches for the detection of safety vests using a combination of five color spaces (RGB, nRGB, HSV, Lab, and YCbCr) and six classifiers (ANN, C4.5, KNN, LR, NB, and SVM). Yu and Zhang [ 25 ] improved the YOLOv4 algorithm to achieve better results in mask detection.…”

Section: Related Workmentioning

confidence: 99%

100+ FPS detector of personal protective equipment for worker safety: A deep learning approach for green edge computing

Xiao

Chen

Guo

2021

Peer-to-Peer Netw. Appl.

View full text Add to dashboard Cite

“…Hence, construction safety is a top priority on all job sites, and machine learning offers a high-tech solution to this problem. That is why applying machine learning in construction safety has been attracted numerous researches to date [8,14,[19][20][21][22][23][24][25][26][27][28]. In the study [21], neural network and decision tree analyses were implemented to assess the unsafe act of not anchoring harnesses while working on a scaffold of 40 migrant workers, whereas, with an accident data from the Singapore construction industry, a neural network analysis was performed on a quantitative occupational safety and health management system audit [22].…”

Section: B Safety Management For Construction Sitesmentioning

confidence: 99%

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Van

Quoc

2021

JASTT

View full text Add to dashboard Cite

Machine learning plays a vital role in construction industry which could make improve project’s safety, productivity, and quality. Many studies have attempted to explore the potential opportunities to adopt this technology in different aspects of the construction sector. However, no comprehensive study to review the global research trends on this technological advancement in construction management domain. The goal is to investigate and summarize the state-of-the-art knowledge body in this topic in a systematic manner. To achieve this, this paper considered 161 studies on machine learning in construction management related to bibliographic records retrieved from the Scopus database by adopting scientometric analysis approach. This paper found that since 2014, there has been a considerable increase in the number of publications on this domain. Researchers from the United States, China, and Australia have been the main contributors to this research area through regional analysis. This study also revealed that approximately 34% of all countries in the world are engaged in this domain research. In addition, five main aspects in construction management have been applied machine learning techniques, namely, assess and reduce risk, safety management for construction sites, cost estimation and prediction, Schedule management, and building energy demand prediction. Furthermore, three potential construction management research areas that can apply this technology were proposed for further studies. The findings will help both professionals and researchers more understanding how machine learning knowledge is evolving and its role played in the construction management domain, and this study thus offers a useful reference point to how can develop this area in the future.

show abstract

A Comparative Study of Machine Learning Classification for Color-based Safety Vest Detection on Construction-Site Images

Cited by 31 publications

References 34 publications

Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

100+ FPS detector of personal protective equipment for worker safety: A deep learning approach for green edge computing

Research Trends on Machine Learning in Construction Management: A Scientometric Analysis

Contact Info

Product

Resources

About