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

Abstract

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 worker's unsafe behaviors. A deep neural network, Mask R-CNN, was used as classification and segmentation of worker's tasks combined with object correlation detection (OCD) module to identify worker's unsafe behaviors. The approach divides the overall construction worker's safety into two subsets, classification of worker and detection of safe (class-1) and unsafe (class-2) behavior using OCD block. The overall performance was evaluated on set of real scenarios with test results showing 85 % and 97 % precision and recall for class-1 (safe behavior) and 91 % and 65 % precision and recall for class-2 (unsafe behavior). The overall accuracy of 86 % confirms the Mask R-CNN-based OCD module's applicability for detecting worker's unsafe behavior effectively in a construction environment.