Analysis of convolutional neural networks for the recognition of violation of labor safety rules in the workplace.
Abstract
Labor protection is a series of measures and tools aimed at preserving human health and performance. In various industries, employees are exposed to various negative factors on their health, for example, wearing a helmet can save an employee from the destruction of the skull bones, a concussion, all these injuries are classified as severe with all the ensuing consequences. The work is devoted to finding the best model of a convolutional neural network, which will have the best performance in determining the objects on which the model has been trained and is optimal for using the software. The data was marked using the labelImg program, all other stages of training and testing were carried out in the Python environment. The dataset includes several labeled classes, namely: a person with and without a helmet, a person with dielectric gloves and without gloves, a person with and without a cigarette, a person in special work shoes and without them, a person in work clothes and in normal clothes..
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