IoT networking for worker safety monitoring using the construction site images and worker health records

The ability to enhance the construction worker's safety performance on-site may be made possible by computer vision-based approaches and deep learning developments. However, due to a variety of technical accuracy, reliability, and administrative issues, the practical application of computer vision and deep learning has been constrained. Hence, to address the above limitations in the conventional techniques, this research develops a latran timber optimisation-based ensemble classifier (ensemble-based-LTO) to construct an IoT-enabled safety framework for construction workers. Utilising the bandwise texture descriptor and pre-trained weights from various architectures such as Resnet-101 and VGG-16, the proposed method extracts the refined features from ROI minimising the computational complexity. The optimised ensemble classifier, effectively learns the long-term contextual dependencies and the spatial properties which in turns increases the detection accuracy. According to the experimental validation, accuracy attainment at 80% of training is 96.74%, sensitivity attainment is 95.33%, and specificity attainment is 97.68%.