Construction industry is definitely a crucial industry in Taiwan due to its economic status. However, according statistics, the number of occupational fatalities of construction industry is the highest compared to the other industries. Among all occupational injuries, a research mentioned that head injuries are prong to be serious and often cause fatality. Even though the governments of diverse countries are dedicated to establishing related regulations, workers may still take off hard hats as it is hard to carry, inconvenient, or uncomfortably sultry, thus increasing the risks of occupational hazards. Accordingly, how to efficiently monitor the hard hat wearing becomes the crucial issue which this research would study. However, safety inspection practices still rely heavily on inspectors' manual monitoring and reporting on construction site at present based on the user interview, and it would be a huge burden of manpower to check whether workers wear hard hat or not all the times just for safety assurance. Therefore, the research aims to exploit deep learning method to develop a non-hard-hat use (NHU) automatic detection system for not only increasing the convenience of construction safety management but also reducing the manpower and time consumed. The research would utilize the data collected from internet and real construction site to train the deep learning network, MRCNN, and combined with different backbones and data augmentation strategies for inspecting whether it would influence Precision, Recall and Accuracy, improving prediction performance. The results indicated that data augmentation strategies do really influence the model performance. Finally, the best model would be selected and connected to LINE App for informing the hard hat wearing violation to on-site managers. the needs of sufficient accuracy, notification of the status of workers not wearing hard hats, and evidences saved for imposing fines.

Construction industry is definitely a crucial industry in Taiwan due to its economic status. However, according statistics, the number of occupational fatalities of construction industry is the highest compared to the other industries. Among all occupational injuries, a research mentioned that head injuries are prong to be serious and often cause fatality. Even though the governments of diverse countries are dedicated to establishing related regulations, workers may still take off hard hats as it is hard to carry, inconvenient, or uncomfortably sultry, thus increasing the risks of occupational hazards. Accordingly, how to efficiently monitor the hard hat wearing becomes the crucial issue which this research would study. However, safety inspection practices still rely heavily on inspectors' manual monitoring and reporting on construction site at present based on the user interview, and it would be a huge burden of manpower to check whether workers wear hard hat or not all the times just for safety assurance. Therefore, the research aims to exploit deep learning method to develop a non-hard-hat use (NHU) automatic detection system for not only increasing the convenience of construction safety management but also reducing the manpower and time consumed. The research would utilize the data collected from internet and real construction site to train the deep learning network, MRCNN, and combined with different backbones and data augmentation strategies for inspecting whether it would influence Precision, Recall and Accuracy, improving prediction accuracy. The results indicated that data augmentation strategies do really influence the model performance. Finally, the best model would be selected and connected to LINE App for informing the hard hat wearing violation to on-site managers. the needs of sufficient accuracy, notification of the status of workers not wearing hard hats, and evidences saved for imposing fines.

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