Abstract
With the rapid development of Internet of things (IoT) and computer vision (CV), the application of combining the IoT platform and CV technology to monitor the worker safety has attracted more and more attention in the field of industrial information. Worker identification is a prerequisite for safety management in industrial production, and safety helmet can not only protect worker’s head from accidental injuries but also help to identify the work types of workers through different colors. Therefore, this study proposes an intelligent method for worker identification based on moving personnel detection and helmet color characteristics. First, the motion objects that contain personnel and nonpersonnel are detected by the Gaussian mixture model (GMM) and extracted to generate the region of interest (RoI) images. Then, the multiple-scale histogram of oriented gradient (MHOG) features of the RoI images are extracted, and the personnel images are identified by the support vector machine (SVM). Third, the workers’ head images are obtained by the OpenPose model and personnel mask, and the GoogLeNet-based transfer learning network is established to extract the head images features and realize worker identification. This method is tested on our dataset, and the average accuracy of worker identification for multiple helmet color combinations reaches 99.43%, which is robust to workers’ angle, scale, and occlusion.
Highlights
Safe production is the top priority of modern industrial development
It can be seen that for each industry, the worker identities can be directly identified through their helmet colors, which is of great significance to the safety monitoring management and personnel scheduling of the enterprise
E accuracy of worker identification will directly affect the accuracy of helmet identification
Summary
Safe production is the top priority of modern industrial development. With the advancement of Industry 4.0 era, it is an inevitable trend to use intelligent vision-based technology to identify worker information, and feed it back to the data center through Internet of things (IoT) for analysis and processing. is automatically improves the efficiency of human work and strengthens the guarantee of workers’ life safety. The vision-based technology of worker safety monitoring has shown significant advantages in industrial production and has played an important role in promoting the development of industrial IoT. In the power industry, white helmet represents the leader, blue helmet represents the management personnel, yellow helmet represents the construction worker, and red helmet represents the outsider. In the construction industry, red helmet represents the leader, Wireless Communications and Mobile Computing yellow helmet represents the ordinary worker, blue helmet represents the technician, and white helmet represents the manager or safety supervisor. It can be seen that for each industry, the worker identities can be directly identified through their helmet colors, which is of great significance to the safety monitoring management and personnel scheduling of the enterprise
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