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The safety of workers at construction sites is one of the most important aspects that should be considered while performing their required tasks. Many rules and regulations have been implemented in the UAE to reduce injuries and fatalities in the jobsites. However, the number of accidents continues to increase. For instance, an accident category of fall-from-height is considered as the top cause of injuries and fatalities. Thus, this paper develops a novel technique that monitors the workers whether they are complying with a safety standard of the Personal Fall Arrest System (PFAS). This paper establishes a real time detection algorithm based on a Convolutional Neural Network (CNN) model in order to detect two main components of the PFAS that are, safety harness and life-line, in addition to a standard safety measure of using a safety helmet. The YOLOv3 algorithm is adopted for a deep learning network used to train the desired model. The model achieved an accuracy rate of 91.26&#x0025; and around 99&#x0025; precision. Moreover, the overall recall of the model was 90.2&#x0025;. The obtained results verify the effectiveness of our proposed model in construction sites to control potential violations and to avoid unnecessary accidents. The main contribution of this paper is to provide an AI-based image detection framework to mitigate the likelihood of fall-from-height accidents.

A Novel Implementation of an AI-Based Smart Construction Safety Inspection Protocol in the UAE