An intelligent vision-based approach for helmet identification for work safety

Helmets are essential equipments to protect workers from danger during inspection and operation. Considering that some workers would not always obey the regulation, video surveillance systems covering the whole factory and supervisors are needed to monitor whether workers are wearing helmets or not. However, with a large number of surveillance screens, it is difficult to identify any helmet violation behavior during any time, which can lead to severe accidents. With the rapid development of image recognition technologies, computer vision-based inspections have been one of the most important industrial application areas. In this paper, an intelligent vision-based approach for helmet identification is proposed. This approach focuses on monitoring whether workers are wearing helmets or not, at the same time, identifying the colors of helmets. A color-based hybrid descriptor composed of local binary patterns (LBP), hu moment invariants (HMI) and color histograms (CH) is proposed to extract features of helmets with different colors (red, yellow and blue). Then a hierarchical support vector machine (H-SVM) is constructed to classify all features into four classes (red-helmet, yellow-helmet, blue-helmet and non-helmet). This approach is tested on our data set and the average accuracy of helmet identification is 90.3%.