Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites

Abstract

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers’ unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites.

Current Safety Practices in the Construction Industry: A Case Study Approach

“…The challenges during safety management in the construction sector have been a significant source of concern for both researchers and practitioners. Studies have revealed that unsafe acts and unsafe working conditions account for more than 90% of accidents on construction job sites [36]. The accident rate has not decreased despite the provision of safety regulations, i.e., OSHA, HSE, ISO 18000.…”

Artificial intelligence in advancing occupational health and safety: an encapsulation of developments

“…Computer vision holds great promise in enhancing workplace safety through AI applications. 104 It enables various functions, including monitoring employee conduct, identifying potential risks, and issuing real-time alerts. An excellent example of its implementation is the use of thermal cameras to identify heat stress in workers.…”

Application of Artificial Intelligence (AI) in Construction Management: A Systematic Literature Review

“…The application of AI in construction management, in general, can aid in optimizing [14], [15] and automating [4] various construction management tasks. This includes project scheduling, delay analysis, Work Breakdown Structure (WBS) creation, project budgeting, updating project progress and schedules, identifying deviations, creating risk maps, and team member allocation [16]- [18].…”