Computer vision system in mobile devices - Smirnova, Tatsiana; Tkachenko, Sergei (Journal of the Belarusian State University. Ecology, 2023)
The article presents the results of designing a computer vision system using deep learning to control supervised systems. The results are based on the use of convolutional neural network and aimed at solving problems, both directly and indirectly related to the solution of environmental problems. The principle is described and the basic model of using computer vision technology in actual projects is shown: 1) social distancing norm compliance in COVID pandemic;2) non-contact manipulator to control technical objects. The COVID-19 Social Distancing Project was developed as a means of automatic control of social distance in crowded places. The article describes the principle of operation of the detector, based on computer vision technology, and makes it possible to monitor compliance with the norm of physical distance. To work the detector requires a computer with the necessary software and the results of images from a video camera. The operation of the detector has been tested under real conditions; the test results are satisfactory. The error in determining the distance did not exceed 9 %. It is assumed that such a device can be used by various services, including sanitary services, to monitor the compliance of the population with the recommendations of the health care system regarding the rules of distancing. The «Contactless Manipulator» project was designed for remote control of objects when working in hard-to-reach and hazardous places (nuclear power plants, toxic waste processing), as well as for people with limited physical abilities. Three functions of “Noncontact manipulator” were implemented in the work – tracking of key points of hand, interception of computer mouse manipulator actions, and control of additional computer functions. The work of the manipulator was also tested in real conditions, the degree of reliability of the results is high. In the future, the manipulator is supposed to be used in a mobile hardware-software complex designed for image processing with subsequent data analysis. The expected fields of practical application of the complex are ecological monitoring, investigation of soil erosion processes, observation of vegetation changes, analysis of plant diseases, waste sorting, and human ecology. The resources of modern information technologies, implemented through the presented projects, demonstrate the possibility of replacing the production of some material devices with their virtual counterparts, safe in terms of ecology.
Keywords: computer vision; artificial neural network; convolutional neural network; image analysis; L1-regularization; YOLO object detector; NVIDIA CUDA; key hand points; MEDIAPIPE framework; augmented reality