DEVELOPMENT OF A HIGH-PERFORMANCE METHOD FOR DETERMINING THE GEOMETRIC PARAMETERS OF OBJECTS IN THE IMAGE

  • S.V. Onishchenko Adygea State University
  • E.V. Melnik Southern Federal University
  • А.V. Kozlovsky Southern Federal University
Keywords: Contactless measurement methods, geometric parameters of objects, Canny detector, Hough algorithm, YOLOv5, mathematical morphology, convolutional neural networks

Abstract

Currently, the development of various process automation systems is becoming more widely
used every day in various fields and industries, the task of developing software methods for the
corresponding automated systems remains urgent. One of the industries where the use and application
of process automation systems is in demand is the field of non-contact measurement of objects
and their parameters. As an example, the task of determining the geometric parameters of
round timber stacked was chosen. In this regard, in this paper, methods were proposed for determining
the geometric parameters of objects based on mathematical morphology operations, organized
using the Canny detector and the Hough algorithm, and a method using a neural network
approach based on the architecture of the YOLOv5 convolutional neural network. As a result of
the conducted experimental studies, for the organization of which specially 3d-printed models of
logs were used, it was found that the method based on the use of neural networks is more accurate
than the method based on mathematical morphology. When solving the problem of counting the
number of objects in the image, using the method based on the neural network approach, all objects
located in the image were determined, whereas the method using mathematical morphology operations was able to determine only 13 of the 16 logs located, and I identified one false object,
as a result of which the result error was about 19% for an image obtained from the Internet. When
conducting an experiment on manufactured cylinder models, the method based on mathematical
morphology operations showed unsatisfactory results. Another advantage of the method based on
the neural network approach is the possibility of calculating the area of the ends of logs in the
image and determining the volume of each of the logs located in the stack, as well as the total total
volume of the entire pack of measured round timber.

References

1. Peter Kaťuch, Miroslav Dovica, Stanislav Slosarč’k, JozefKováč. Comparision of Contact and
Contactless Measuring Methods for Form Evaluation, Procedia Engineering, 2012, Vol. 48,
pp. 273-279. ISSN 1877-7058. Available at: https://doi.org/10.1016/j.proeng.2012.09.514.
2. Adam D., Nissan S. B., Friedman Z., Behar V. The Combined Effect of spatial compounding
and nonlinear Filtering on the Speckle Reduction in Ultrasound Images, Ultrasonic, 2006,
Vol. 44, pp. 166-181.
3. Beder Chr., Bartczak B. and Koch R. A Comparison of PMD-Cameras and Stereo-Vision for
the Task of Surface Reconstruction using Patchlets, Computer Vision and Pattern Recognition.
IEEE, 2007, pp. 1-8.
4. Chaban L.N. Teoriya i algoritmy raspoznavaniya obrazov: ucheb. posobie [Theory and algorithms
of pattern recognition: textbook]. Moscow: MIIGAiK. 2004, 70 p.
5. Kozlovskiy A.V., Onishchenko S.V., Avdeev V.E. Issledovanie metodov izmereniya kruglykh
lesomaterialov [Research of methods for measuring round timber], Fundamental'nye i
prikladnye aspekty komp'yuternykh tekhnologiy i informatsionnoy bezopasnosti: Sb. statey VII
Vserossiyskoy nauchno-tekhnicheskoy konferentsii, Taganrog, 05–11 aprelya 2021 g. [Fundamental
and applied aspects of computer technologies and information security : collection of
articles in the VII All-Russian Scientific and Technical Conference, Taganrog, 05-11 April
2021]. Taganrog: YuFU, 2021, pp. 357-360.
6. Onishchenko, S.V., Kozlovskiy A.V. Issledovanie metoda opredeleniya geometricheskikh
parametrov ob"ektov po predvaritel'no obrabotannym tsifrovym izobrazheniyam [Research of
a method for determining geometric parameters of objects from preprocessed digital images],
Informatsionnye tekhnologii, sistemnyy analiz i upravlenie (ITSAU-2021): Sb. trudov XIX
Vserossiyskoy nauchnoy konferentsii molodykh uchenykh, aspirantov i studentov [Information
technologies, system analysis and management (ITSAU-2021): Proceedings of the XIX All-
Russian Scientific Conference of Young Scientists, postgraduates and students]. Rostov-on-
Don - Taganrog: YuFU, 2020, pp. 59-62.
7. Mel'nik E.V., Onishchenko S.V., Kozlovskiy A.V. Issledovanie vozmozhnosti realizatsii
mobil'nykh kompleksov dlya beskontaktnogo izmereniya geometricheskikh parametrov
ob"ektov [Investigation of the possibility of implementing mobile complexes for contactless
measurement of geometric parameters of objects], Fundamental'nye i prikladnye aspekty
komp'yuternykh tekhnologiy i informatsionnoy bezopasnosti: Sb. statey VIII Vserossiyskoy
nauchno-tekhnicheskoy konferentsii [The proceedings of the VIII All-Russian Scientific and
Technical Conference "Fundamental and applied aspects of computer technology and information
security]. Taganrog, 2022.
8. Herbon C., Tonnies K., Stock B. Detection and segmentation of clustered objects by using
iterative classification, segmentation, and Gaussian mixture models and application to wood
log detection, Pattern Recognition. Springer International Publishing, 2014, pp. 354-364.
9. Galsgaard B., Lundtoft D.H., Nikolov I., Nasrollahi K., Moeslund T.B. Circular Hough Transform and
Local Circularity Measure for Weight Estimation of a Graph-Cut Based Wood Stack Measurement,
IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, 2015, pp. 686-693.
10. Antipov V.A. Nachertatel'naya geometriya: kurs lektsiy [Descriptive geometry: a course of
lectures]. Samara: SamGAPS, 2005, 64 p.
11. Lestari Wiji and Sri Sumarlinda. Application of Mathematical Morphology Algorithm for
Image Enhancement of Breast Cancer Detection, 2019.
12. Kavitha D. Multiple Object Recognition Using OpenCV [Recognition of Multiple Objects
Using OpenCV], Revista Gestão Inovação e Tecnologias [Revision of the Concept of innovation
and technology], 2021, No. 11, pp. 1736-1747. 10.47059/revistageintec.v11i2.1795.
13. Li J., Ding S. A research on improved canny edge detection algorithm. In: Applied Informatics
and Communication, ICAIC 2011. Communications in Computer and Information Science,
Vol. 228. Springer, Berlin, 2011.
14. Canny John. A Computational Approach to Edge Detection. Pattern Analysis and Machine Intelligence,
IEEE Transactions on. PAMI-8, 1986, pp. 679-698. 10.1109/TPAMI.1986.4767851.
15. Shehata Allam & Mohammad Sherien & Abdallah Mohamed & Ragab Mohammad. A Survey
on Hough Transform, Theory, Techniques and Applications, 2015.
16. Zhu Xingkui, Shuchang Lyu, Xu Wang and Qi Zhao. PH-YOLOv5: Improved YOLOv5 Based on
Transformer Prediction Head for Object Detection on Drone-captured Scenarios, 2021 IEEE/CVF
International Conference on Computer Vision Workshops (ICCVW), 2021, pp. 2778-2788.
17. Samoylov A.N., Sergeev N.E., Voloshin A.V., Kozlovskiy A.V. Metod fotogrammetricheskogo
izmereniya geometricheskikh parametrov ob"ektov, invariantnyy k fotoregistriruyushchim
ustroystvam [The method of photogrammetric measurement of geometric parameters of objects,
invariant to photo-recording devices], Vestnik Adygeyskogo gosudarstvennogo
universiteta. Ser.: Estestvenno-matematicheskie i tekhnicheskie nauki [Bulletin of the Adygea
State University. Ser.: Natural-mathematical and technical sciences], 2021, Issue. 4 (291),
pp. 58-69. Available at: http://vestnik.adygnet.ru.
18. Buchatskiy P.Yu., Onishchenko S.V., Teploukhov S.V. Razrabotka metoda opredeleniya
geometricheskikh parametrov ob"ektov na izobrazhenii na osnove neyrosetevogo podkhoda
[Development of a method for determining geometric parameters of objects in an image based
on a neural network approach], Vestnik Adygeyskogo gosudarstvennogo universiteta.
Tekhnicheskie nauki [Bulletin of the Adygea State University. Technical sciences], 2022,
No. 3, pp. 57-62.
19. Buchatskiy P.Yu., Onishchenko S.V., Teploukhov S.V. Razrabotka metoda podscheta kolichestva
ob"ektov na tsifrovom izobrazhenii na osnove morfologicheskogo podkhoda [Development of a
method for counting the number of objects in a digital image based on a morphological approach],
Distantsionnye obrazovatel'nye tekhnologii: Mater. VII mezhdunarodnoy nauchnoprakticheskoy
konferentsii, YAlta, 20–22 sentyabrya 2022 goda [Distance learning technologies:
Materials of the VII International Scientific and Practical Conference, Yalta, September 20-22,
2022]. Simferopol': Obshchestvo s ogranichennoy otvetstvennost'yu «Izdatel'stvo Tipografiya
«Arial», 2022, pp. 120-123.
20. Onishchenko S.V., Kozlovskiy A.V., Mel'nik E.V. Razrabotka beskontaktnoy sistemy
izmereniya geometricheskikh parametrov ob"ektov na izobrazhenii [Development of a contactless
system for measuring geometric parameters of objects in an image], Izvestiya Tul'skogo
gosudarstvennogo universiteta. Tekhnicheskie nauki [Izvestiya Tula State University. Technical
sciences], 2022, pp. 177-181.
Published
2023-02-17
Section
SECTION I. MODELS AND METHODS OF INFORMATION PROCESSING