Abstract
In the industrial production, construction and other fields, safety helmet as an important safety protective equipment, the wearing situation of workers personal safety and property safety is of great significance. Therefore, this paper proposes a safety helmet detection method based on the improved YOLOX algorithm. First, the 5000 pictures of safety helmet wearing at construction sites are labelled. The Squeeze and-Excitation module is introduced in the YOLOX network structure. The original Loss function is replaced with varifocal Loss. After experimental verification, compared with the original YOLOX target detection algorithm, our algorithm improves by 2.13 percentage points, enhances the model's focus on key areas and optimizes the model training effect, while the number of model parameters does not increase significantly. In conclusion, our algorithm has a wide range of application prospects and research value.
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