YOLO V3 + VGG16-based automatic operations monitoring and analysis in a manufacturing workshop under Industry 4.0

Under the background of Industry 4.0 and smart manufacturing, operators are still the core of manufacturing production, and the standardization of their actions greatly affects production efficiency and quality. However, they have not received enough attention. In view of the monitoring and analysis of operators’ actions in the manufacturing field, this paper proposes the YOLO V3 + VGG 16 transfer learning network. First, the region detection of key operators is realized by using YOLO V3, and an action dataset is constructed. Second, using transfer learning to realize the automatic recognition, monitoring and analysis of small sample data, the recognition accuracy of the proposed method is greater than 96%, and the average deviation of the action execution time is less than 1 s. This research is expected to provide guidance for increasing the degree of workshop automation, improving the standardization of operators’ actions, optimizing action processes and ensuring product quality. • First manufacturing workshop action dataset based on RGB images was constructed. • A YOLOV3 + VGG16 action recognition framework was proposed to recognize industrial operations with high accuracy. • Automatic action analysis is helpful to process monitoring and quality improvement. • Effectively reduce the labor cost of the enterprise, improve the compliance rate of standard operations, the quality and efficiency of the production process.