In the process of dissertation research for the first time a model of preparation of future engineers-teachers for the development and application of intelligent machine vision systems was developed and theoretically substantiated; determined pedagogical conditions for the development and application of intelligent machine vision systems by future engineers-teachers of computer profile: motivation of educational activities through updating the content of training of engineers-teachers of disciplines of professional orientation; use of interdisciplinary links between computer vision and disciplines of the training cycle; integration of engineering knowledge of computer vision into educational and methodological support for the implementation of intelligent systems; developed educational and methodological support for the implementation of intelligent machine vision systems on the platform of the OpenCV library; specified: the concept of "intelligent machine vision system"; areas of application of intelligent machine vision systems in the training of computer engineers- teachers; content of the discipline "Technology of Artificial Intelligence"; methods and stages of designing computer vision using the OpenCV library; theoretical and methodological bases of development and application of intelligent machine vision systems by future engineers-teachers of computer profile have been further developed. Selection of software modules of the OpenCV library as a computer vision system, its constituent elements for the training of future engineers-teachers in the field of computer technology; implemented computer vision systems based on the Open CV library to outline the subject area of the discipline; developed components of the methodology for studying the OpenCV library for the development and application of intelligent machine vision systems in the training of future engineers-teachers of computer science; implemented computer vision systems based on the Open CV library to outline the subject area of the discipline; developed components of the methodology for studying the OpenCV library for the development and application of intelligent machine vision systems in the training of future engineers-teachers of computer science; the methodical content of the discipline "Artificial Intelligence Technologies" was specified and a set of tasks of the laboratory cycle of the content module "Computer Vision Technologies" for students of higher education in the specialty 015.39 Professional Education (Digital Technologies) was developed.
An analysis of scientific sources confirmed that the educational training of pedagogical engineers should be versatile and focused, and as information technology is increasingly shifted towards the study and application of artificial intelligence, robotics, computer vision and more. And the prospect is that the need for specialists who have such technologies will only increase, in contrast to the need for ordinary skilled workers. Therefore, the quality of training of engineers-teachers should correspond to the trends of modern development of artificial intelligence technologies, in particular, machine vision systems.
The main components of readiness (target, content, operational and control and regulatory) of future computer engineers-teachers have been studied. According to the components of readiness of future engineers-teachers to develop and apply intelligent machine vision systems, criteria (value, knowledge, operational, evaluation-analytical) and indicators of their formation are determined, which provide an opportunity to assess the development of this competence and make certain adjustments. Assessing the level of readiness for the development and application of intelligent machine vision systems in future engineers-teachers, we focused on indicators that reflect the main components of their structure.
Taking into account the results of empirical research and the specifics of training future computer engineers in pedagogical university and understanding the nature and structure of computer vision, we have identified four levels of readiness of future engineers to develop and apply intelligent machine vision systems: high ( creative), necessary (sufficient), critical (insufficient), low (unacceptable). The quality of mastering computer vision systems by future engineers-educators should be assessed by the indicators of mastery of the algorithm of computer vision systems. Indicators are expressed through: step of work → description → example, which will allow to focus on the meaning, characteristics, capabilities and main areas of its application. The ability of future pedagogical engineers to develop intelligent machine vision systems and apply them in their own professional activities was determined by the results of the control section.
