THE SOBEL ALGORITHM IMPLEMENTATION FOR DETECTION AN OBJECT CONTOUR IN THE MOBILE ROBOT’S WORKSPACE IN REAL TIME
PDF
DOI

Keywords

Industry 5.0, Сomputer Vision Systems, Mobile Robots, Work zone

How to Cite

THE SOBEL ALGORITHM IMPLEMENTATION FOR DETECTION AN OBJECT CONTOUR IN THE MOBILE ROBOT’S WORKSPACE IN REAL TIME. (2024). TECHNICAL SCIENCE RESEARCH IN UZBEKISTAN, 2(3), 23-33. https://universalpublishings.com/index.php/tsru/article/view/4675

Abstract

This article is devoted to the Sobel algorithm implementation for detection an object contour in the mobile robot’s workspace in real time. Mathematical models of the algorithm functioning were examined in detail, and the developed program in Python in the PyCharm environment was subjected to a series of experiments. The experimental results indicate the outstanding performance of the algorithm, reaching 30.00 frames per second when processing a video stream. However, the contouring speed of 1.19 frames per second indicates potential performance challenges when working in dark conditions using artificial side lighting. The research results presented in the article highlight the effectiveness of the algorithm in real-time conditions and highlight the importance of taking into account lighting features when using it in mobile robots.

PDF
DOI

References

Deineko, Zh., & et al.. (2021). Color space image as a factor in the choice of its processing technology. Abstracts of I International scientific-practical conference «Problems of modern science and practice» (September 21-24, 2021). Boston, USA, pp. 389-394.

Orobinskyi, P., & et al.. (2020). Comparative Characteristics of Filtration Methods in the Processing of Medical Images. American Journal of Engineering Research, 9(4), 20-25.

Lyashenko, V., Kobylin, O., & Ahmad, M. A. (2014). General methodology for implementation of image normalization procedure using its wavelet transform. International Journal of Science and Research (IJSR), 3(11), 2870-2877.

Rabotiahov, A., Kobylin, O., Dudar, Z., & Lyashenko, V. (2018, February). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET) (pp. 665-670). IEEE.

Rabotiahov, A., Kobylin, O., Dudar, Z., & Lyashenko, V. (2018, February). Bionic image segmentation of cytology samples method. In 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET) (pp. 665-670). IEEE.

Гиренко, А. В., Ляшенко, В. В., Машталир, В. П., & Путятин, Е. П. (1996). Методы корреляционного обнаружения объектов. Харьков: АО “БизнесИнформ, 112.

Lyashenko, V. V., Babker, A. M. A. A., & Kobylin, O. A. (2016). The methodology of wavelet analysis as a tool for cytology preparations image processing. Cukurova Medical Journal, 41(3), 453-463.

Lyashenko, V., & et al.. (2016). The Methodology of Image Processing in the Study of the Properties of Fiber as a Reinforcing Agent in Polymer Compositions. International Journal of Advanced Research in Computer Science, 7(1), 15-18.

Lyubchenko, V., Matarneh, R., Kobylin, O., & Lyashenko, V. (2016). Digital image processing techniques for detection and diagnosis of fish diseases. International Journal of Advanced Research in Computer Science and Software Engineering, 6(7), 79-83.

Lyashenko, V., Matarneh, R., & Kobylin, O. (2016). Contrast modification as a tool to study the structure of blood components. Journal of Environmental Science, Computer Science and Engineering & Technology, 5(3), 150-160.

Mousavi, S. M. H., Lyashenko, V., & Prasath, S. (2019). Analysis of a robust edge detection system in different color spaces using color and depth images. Компьютерная оптика, 43(4), 632-646.

Lyashenko, V. V., Matarneh, R., Kobylin, O., & Putyatin, Y. P. (2016). Contour Detection and Allocation for Cytological Images Using Wavelet Analysis Methodology. International Journal, 4(1), 85-94.

Babker, A., & Lyashenko, V. (2018). Identification of megaloblastic anemia cells through the use of image processing techniques. Int J Clin Biomed Res, 4, 1-5.

Abu-Jassar, A., & et al. (2023). Obstacle Avoidance Sensors: A Brief Overview. Multidisciplinary Journal of Science and Technology, 3(5), 4-10.

Yevsieiev, V., & et al. (2024). Using Contouring Algorithms to Select Objects in the Robots’ Workspace. Technical Science Research In Uzbekistan, 2(2), 32-42.

Maksymova, S., & et al. (2022). Development of an Automated System of Terminal Access to Production Equipment Using Computer Vision. In Manufacturing & Mechatronic Systems 2022: Proceedings of VIst International Conference, 22-23.

Akopov, M., & et al. (2023). Choosing a Camera for 3D Mapping. Journal of Universal Science Research, 1(11), 28-38.

Yevsieiev, V., & et al. (2024). Active Contours Method Implementation for Objects Selection in the Mobile Robot’s Workspace. Journal of Universal Science Research, 2(2), 135-145.

Nevliudov, I. Sh., & et al. (2023). Conveyor Belt Object Identification: Mathematical, Algorithmic, and Software Support. Appl. Math. Inf. Sci. 17(6), 1073-1088.

Maksymova, S., & et al. (2023). Selection of Sensors for Building a 3D Model of the Mobile Robot's Environment. In Manufacturing & Mechatronic Systems 2023: Proceedings of VIIst International Conference (M&MS), Kharkiv, 33-35.

Yevsieiev, V., & et al. (2024). Object recognition and Tracking Method in the Mobile Robot’s Workspace in Real Time. Technical Science Research In Uzbekistan, 2(2), 115-124.

Baker, J. H., Laariedh, F., Ahmad, M. A., Lyashenko, V., Sotnik, S., & Mustafa, S. K. (2021). Some interesting features of semantic model in Robotic Science. SSRG International Journal of Engineering Trends and Technology, 69(7), 38-44.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2021). Neural Networks As A Tool For Pattern Recognition of Fasteners. International Journal of Engineering Trends and Technology, 69(10), 151-160.

Sotnik, S., & et al.. (2022). Analysis of Existing Infliences in Formation of Mobile Robots Trajectory. International Journal of Academic Information Systems Research, 6(1), 13-20.

Sotnik, S., & et al.. (2022). Modern Industrial Robotics Industry. International Journal of Academic Engineering Research, 6(1),. 37-46.

Al-Sharo, Y. M., Abu-Jassar, A. T., Sotnik, S., & Lyashenko, V. (2023). Generalized Procedure for Determining the Collision-Free Trajectory for a Robotic Arm. Tikrit Journal of Engineering Sciences, 30(2), 142-151.

Al-Sharo Y., & et al. (2023). A Robo-hand prototype design gripping device within the framework of sustainable development. Indian Journal of Engineering, 20, e37ije1673.

Lyashenko, V., & et al. (2023). Automated Monitoring and Visualization System in Production. Int. Res. J. Multidiscip. Technovation, 5(6), 09-18.

Sotnik, S., Matarneh, R., & Lyashenko, V. (2017). System model tooling for injection molding. International Journal of Mechanical Engineering and Technology, 8(9), 378-390.

Lyashenko, V., Ahmad, M. A., Sotnik, S., Deineko, Z., & Khan, A. (2018). Defects of communication pipes from plastic in modern civil engineering. International Journal of Mechanical and Production Engineering Research and Development, 8(1), 253-262.

Sotnik, S., Mustafa, S. K., Ahmad, M. A., Lyashenko, V., & Zeleniy, O. (2020). Some features of route planning as the basis in a mobile robot. International Journal of Emerging Trends in Engineering Research, 8(5), 2074-2079.

Dadkhah, M., Lyashenko, V. V., Deineko, Z. V., Shamshirband, S., & Jazi, M. D. (2019). Methodology of wavelet analysis in research of dynamics of phishing attacks. International Journal of Advanced Intelligence Paradigms, 12(3-4), 220-238.

Lynn, N. D., & et al. (2021). Implementation of Real-Time Edge Detection Using Canny and Sobel Algorithms. In IOP Conference Series: Materials Science and Engineering, IOP Publishing, 1096(1), 012079.

Pengo, T., & Chaikan, P. (2021). High performance and energy efficient sobel edge detection. Microprocessors and Microsystems, 87, 104368.

Han, L., & et al. (2020). Research on edge detection algorithm based on improved sobel operator. In MATEC Web of Conferences, EDP Sciences, 309, 03031.

Ravivarma, G., & et al. (2021). Implementation of Sobel operator based image edge detection on FPGA. Materials Today: Proceedings, 45, 2401-2407.

As, R. A., & Gopalan, S. (2022). Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images. Procedia Computer Science, 201, 487-494.

Sanida, T., & et al. (2020). A heterogeneous implementation of the Sobel edge detection filter using OpenCL. In 2020 9th International Conference on Modern Circuits and Systems Technologies (MOCAST), IEEE, 1-4.

Ranjan, R., & Avasthi, V. (2023). Edge Detection Using Guided Sobel Image Filtering. Wireless Personal Communications, 132(1), 651-677.

Tian, R., & et al. (2021). Sobel edge detection based on weighted nuclear norm minimization image denoising. Electronics, 10(6), 655.

Chetia, R., & et al. (2021). Quantum image edge detection using improved Sobel mask based on NEQR. Quantum Information Processing, 20, 1-25.

Joshi, R., & et al. (2022). Fast Sobel edge detection for iot edge devices. SN Computer Science, 3(4), 302.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.