A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Computer vision approaches for detecting missing barricades
Abstract The installation of barricades effectively prevents falls from height (FFH) on construction sites. Common approaches for detecting missing barricades (e.g., manual inspection of the site or three-dimensional models) are not practical due to two inherent challenges: (1) these approaches are labor-intensive and time-consuming; and (2) FFH hazards are dynamic and changing as construction work progresses. To address these challenges, two computer vision-based detection approaches, including Masks Comparison Approach (MCA) and Missing Object Detection Approach (MODA), are developed in this study to automatically detect missing barricade. The performance of the proposed approaches and their benefits and implementation challenges were evaluated through a case study. The results demonstrate that MODA can achieve better performance and have several implementation advantages over MCA. The average precision and average recall for MODA were 57.9% and 73.6%, respectively. These two approaches can help site managers take action promptly to reduce the risks of FFH accidents.
Highlights Two computer vision-based detection approaches are developed in this study to automatically detect missing barricade. MODA can achieve a better performance and have several implementation advantages over MCA. A case study is implemented to evaluate the performance of the approaches.
Computer vision approaches for detecting missing barricades
Abstract The installation of barricades effectively prevents falls from height (FFH) on construction sites. Common approaches for detecting missing barricades (e.g., manual inspection of the site or three-dimensional models) are not practical due to two inherent challenges: (1) these approaches are labor-intensive and time-consuming; and (2) FFH hazards are dynamic and changing as construction work progresses. To address these challenges, two computer vision-based detection approaches, including Masks Comparison Approach (MCA) and Missing Object Detection Approach (MODA), are developed in this study to automatically detect missing barricade. The performance of the proposed approaches and their benefits and implementation challenges were evaluated through a case study. The results demonstrate that MODA can achieve better performance and have several implementation advantages over MCA. The average precision and average recall for MODA were 57.9% and 73.6%, respectively. These two approaches can help site managers take action promptly to reduce the risks of FFH accidents.
Highlights Two computer vision-based detection approaches are developed in this study to automatically detect missing barricade. MODA can achieve a better performance and have several implementation advantages over MCA. A case study is implemented to evaluate the performance of the approaches.
Computer vision approaches for detecting missing barricades
Chian, Eugene (author) / PhD Fang, Weili (author) / PhD Goh, Yang Miang (author) / PhD Tian, Jing (author)
2021-07-28
Article (Journal)
Electronic Resource
English
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Online Contents | 2004