Özet:

Work accidents in many businesses pose many dangers for employees. Examples of these work accidents include slippery floors, falling materials, harmful substances/gas leaks, protective clothing and equipment not being used in improper ways or not used at all. It is very important to identify these hazards and take the necessary measures for both worker safety and the employer. Among these dangerous situations, the easiest and most frequent accident to prevent is accidents that occur as a result of slipping or falling. Many employees are injured as a result of these accidents that occur due to problems such as a foreign liquid/substance on the working surface, the inability of the worker to establish his own balance or surface inequalities etc. In this study, such situations such as falling, slipping and balance disorders will be determined by IoT and a deep learning-based system in order to recognize such accidents and make necessary arrangements. Deep learning methods that detect movement such as sliding etc. will be evaluated according to the performance evaluation criteria and the method with the most accurate result will be determined. With the results to be obtained from this study, it is aimed to make improvements to prevent these accidents.

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