Development of an Algorithm and Software System for Facing Panels Accounting on Production Lines

2023;
: cc. 89 - 95
1
Lviv Polytechnic National University, Ukraine
2
Lviv Polytechnic National University, Ukraine
3
Lviv Polytechnic National University, Ukraine
4
Lviv Polytechnic National University, Ukraine

This paper aims to develop and implement an algorithm and an automated software system for the auto- matic accounting process of external facing panels during transportation on line conveyors. The method described in this paper is designed to simplify the process of production and accounting of wall-facing panels. This method can also serve as a model for implementing other manufacturers. The developed  algorithm consists of the following steps: obtaining a video stream in real-time or from a file and its targeted processing and determining the number of moving objects of interest. The software accounting system created based on the developed algorithm analyzes the video data and stores all the necessary results and settings in the data- base. The software system can adapt to the accounting requirements of other types of similar products in other areas.

  1. Sharma A., Pathak  J., Prakash M., Singh J. N., (2021). “Object Detection using OpenCV and Python”, 3rd Inter- national Conference on Advances in Computing, Commu- nication Control and Networking (ICAC3N), Greater Noida, India, 2021, pp. 501–505, DOI: 10.1109/ ICAC3N53548.2021.9725638.
  2. Archana K., Prasad K., (2022). “Object Detection Using Region-Conventional Neural Network (RCNN) and OpenCV”, International Journal of Distributed Artificial Intelligence (IJDAI), 14(2), 2022, pp. 1–9, DOI: 10.4018/ IJDAI.315277.
  3. Pang, B., Nijkamp, E., Wu, Y. N., (2020). “Deep Learning With TensorFlow: A Review”, Journal of Educational and Behavioral Statistics, 45(2), pp. 227–248, DOI: 10.3102/ 1076998619872761.
  4. Shallue, C. J., Vanderburg, A., (2018). “Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Ke- pler-90”, The Astronomical Journal,  Volume 155,  Num- ber 2, pp. 94–117, DOI: 1 10.3847/1538-3881/aa9e09.
  5. Chen  D.,  Zheng  P.,  Chen  Z.,  Lai  R.,  Luo  W.,  Liu H., (2021). “Privacy-Preserving Hough Transform and Line Detection on Encrypted Cloud Images”, IEEE 20th International Conference on Trust, Security, and Privacy in Computing and Communications (TrustCom), Shen- yang, China, 2021, pp. 486–493, DOI: 10.1109/ Trust- Com53373.2021.00078.
  6. Marichal-Hernández J.G., Oliva-García  R.,  Gómez- Cárdenes Ó., Rodríguez-Méndez I., Rodríguez-Ramos J.M., (2021). “Inverse Multiscale Discrete Radon Trans- form by Filtered Backprojection”. Applied Sciences. 2021; 11(1):22, pp. 34–44, DOI: 10.3390 /app11010022.
  7. Kulik S., Shtanko A., (2020). “Using convolutional neural networks for recognition of objects varied in appearance in computer vision for intellectual robots”, Procedia Com- puter  Science,   Volume   169,   2020,   pp. 164–167, DOI: 10.1016/j.procs.2020.02.129.
  8. Ivanov, Y., Sharov, B., Zalevskyi N., Kernytskyi, O., (2022). “Software System for End-Products Accounting in Bakery Production Lines Based on Distributed Video Streams Analy- sis”, Advances in Cyber-physical Systems 2022; Volume 7, Number 2, pp. 101–107, DOI: 10.23939/ acps2022.02.101.