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A Real Time Detection Method for Semi-Supervised Safety Helmet Wearing Based on Pseudo Label

DOI: 10.12677/HJDM.2023.131007, PP. 67-74

Keywords: 安全头盔检测,半监督,伪标签,Safety Helmet Detection, Semi-Supervised, Pseudo-Label

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In the process of metal manufacturing, bridge and tunnel engineering, and construction industry, wearing a safety helmet can greatly protect the safety of life. The target detection method can be used to detect whether a helmet is worn or not. The current safety helmet wearing detection methods mostly focus on supervised learning, which relies on a large number of accurately labeled data. However, in reality, the marked data is very expensive, and the insufficient acquisition of training data may become a bottleneck for performance improvement. Compared with labeled data, unlabeled data are more abundant, cheaper and easier to obtain. Based on this problem, this paper introduces the pseudo-label technology into the traditional safety helmet detection method, and pro-poses a semi-supervised safety helmet detection method. It utilizes both labeled and unlabeled da-ta when training the model, and it requires only a small amount of labeled data, while assisting the training of the model with a large amount of unlabeled data. The experimental results on the self-made helmet data set show that this method can achieve good performance under limited labeled data, with an accuracy rate of 92.7% and an average accuracy increase of 3.7%. It meets the requirements for helmet detection in case of insufficient marking data.


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