Abstract
Wearing safety helmet is a protective measure, operators must wear them when they entering the construction area. Safety helmet can significantly reduce the probability of brain injury of workers, it ensures the safety of operators to some extent. Workers do not wear helmets in some actual construction processes, so it is necessary to detect the helmets in the construction area. Only relying on manual identification not only consumes a lot of manpower and material resources, but also is inefficient. So manual identification cannot meet the requirements of real-time monitoring. In this paper, a method of helmet detection based on human body recognition is proposed in which SSD neural network model is trained to recognize operators quickly. According to the geometric characteristics of human body and combined with HSV color space and morphological processing, helmet wearing is detected. Through the monitoring camera to obtain the video stream of actual work environment as the experimental data, the effectiveness of the above method is verified by the experiment.
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