Automation of Monitoring Compliance with Technological Regulations Using the Example of the Process of Filling...

In this paper, an approach to automating the monitoring of compliance with regulated technological operations using computer vision methods is proposed and investigated, with the loading of petroleum products considered as a case study. A distinctive feature of this work is the integration of a formalized description of the production process in BPMN notation with an object detection system, enabling not only object recognition but also interpretation of the sequence of technological actions performed by personnel. Based on the collected and annotated dataset containing more than 6000 images, a YOLOv11 neural network model was trained to monitor key stages of a technological operation. The experimental results show that the trained model provides high accuracy in detecting objects during the daytime (mAP50 is approximately 0.98), while maintaining the ability to work in real time. The results obtained confirm their applicability in industrial conditions. The work revealed the dependence of the quality of computer vision system functioning on the illumination conditions of the production area. It has been established that at night there is a significant decrease in recognition accuracy due to the presence of glare from lighting sources directed at the camera area. The results obtained make it possible to substantiate the need to take into account lighting factors when designing video monitoring systems for technological processes. To move from the level of object detection to monitoring compliance with regulations, an algorithm for interpreting detected objects has been developed, which ensures the fixation and analysis of the sequence of operations performed. Experimental tests conducted at the existing production site have confirmed the possibility of automated detection of violations of technological regulations and an increase in the level of industrial safety. The directions for further development of the proposed approach have also been identified, including the expansion of the training sample, taking into account a variety of production scenarios, and the development of methods to increase the stability of the system in difficult light conditions.