Research on Intelligent Wearable Detection Method Based on Machine Vision

The working site of aircraft maintenance personnel is an important part of the entire airport operation. Therefore, it is necessary to supervise whether maintenance personnel comply with safety regulations during the course of their work. The most important point of all is to check whether maintenance personnel’s clothing complies with related specifications. According to the requirements of current epidemic prevention, we have made higher standards on the dressing of maintenance personnel, especially for those who enter the key areas of the cabin. As we all know, traditional manual safety supervision requires monitoring on the maintenance site. However, due to the large number of maintenance sites, supervisors are not able to check all the workers’ operations simultaneously. Thus violations cannot be obtained timely, which is bound to cause ineffective supervision. According to the characteristics of the application scenario, this paper proposes an intelligent maintenance crew clothing detection method based on the OpenVINO platform, which can analyze and identify the clothing requirements of maintenance personnel in real time, and promptly warn the clothing that does not meet the safety regulations; in the training of the samples, Using the YOLO algorithm to perform machine learning on the existing model, which can quickly and accurately find problems related to unsafe dressing at the aircraft maintenance site.