عنوان انگلیسی مقاله:
Computer vision approaches for detecting missing barricades
ترجمه فارسی عنوان مقاله:
رویکردهای بینایی ماشین برای تشخیص موانع گمشده
Sciencedirect - Elsevier - Automation in Construction, 131 (2021) 103862: doi:10:1016/j:autcon:2021:103862
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.
Keywords: Falls from height | Safety | Computer vision | Unsafe behavior | Deep learning