(untitled) (ENVIRONMENT. TECHNOLOGY. RESOURCES. Proceedings of the International Scientific and Practical Conference, 2024)
This article examines the use of an AI-powered automated image analysis system. The system's purpose is to enhance the workflow of students during applied physics laboratory experiments, helping them analyze images and perform accurate microobject counting. On the software side, the system incorporates machine learning algorithms for visual processing applications using Python and its’ extension libraries – CV2, Tensorflow, Keras, SkLearn etc.. The hardware consists of a camera and microprocessor, which, in conjunction with the image processing software, perform microobject recognition and counting in real-time. The goal is to automate applied physics laboratory experiments in which the counting of microobjects, be it organic or human-made, is usually done manually. During these applied physics laboratory experiments and with the aid of this system, students are exposed to a modern workflow, further preparing them for future work environments, teaching them about process automation, and further increasing their interest in micro-scale related science subjects. Automation using image processing technology combined with automatic data logging from images allows for fast and accurate micro-object counting.