Utilizing safety rule correlation for mobile scaffolds monitoring leveraging deep convolution neural networks

Falls from height (FFH) are still a leading cause of fatalities in the construction industry, which also includes scaffolding-related accidents. Despite regular safety inspections, numerous scaffolding-related accidents occur at the construction site. The current safety monitoring practices are not only impractical but infeasible due to dynamicity of construction environment. Since a separate computer training and detection process is generally required to acquire spatiotemporal reasoning to control a single hazard; thus previous efforts in vision intelligence applications to improve safety monitoring are still limited to specific hazards. Also, in regard to detecting unsafe situations based on extracted correlations from safety rules, to date, previous studies have devoted little attention to this domain. To address these issues, this study proposes a correlation-based approach for mobile scaffold safety monitoring and detecting …