Аңдатпа
The object of this study is a procedure for measuring physical quantities under laboratory conditions at educational institutions. The issue related to this case is the lack of any comprehensive method and technical solution suitable for the experimental study of physics in both offline and online learning formats. To solve this problem, an approach has been proposed, based on computer vision technology and training special neural models to recognize objects in video frames that perform mechanical movement. The idea of the proposed approach is based on the hypothesis that by measuring the position of an object in video frames with sufficient accuracy, it is possible to determine the functional type of the law of its motion. Further, knowing the function of the law of motion, it is possible to calculate any physical quantities describing the process under consideration. The idea is implemented in the form of a technical solution, which is a set of prototypes of automated laboratory devices. The choice of the method for determining the law of motion was carried out using the analysis of the recognition error, measurement error, speed and resistance to external conditions of the Hough algorithmic method and the YOLOv8n neural network model. It is shown that the neural network method YOLOv8n has very high accuracy but low performance. The Hough method shows high performance but lower accuracy and resistance to external conditions. It was found that the accuracy of the YOLOv8n method is 4 times higher, but the execution speed is 10 times lower than that of the Hough method. However, in the case of artificial lighting and fixing the distance from the camera to objects, the Hough method provides 99.9% accuracy in recognizing an object in video frames. The obtained prototypes of devices can be used for further research to determine their impact on the quality of physics education