THE SMALL AERIAL OBJECTS SEGMENTATION METHOD ON OPTICAL-ELECTRONIC IMAGES BASED ON THE SOBEL EDGE DETECTOR
Article Sidebar
Main Article Content
Abstract
The subject matter in the article is the stage of segmentation of small aerial objects on images obtained from an optical-electronic system. The goal is to develop a small aerial object segmentation method based on optical-electronic images based on the Sobel edge detector. The tasks are: analysis of existing methods of segmentation of optical-electronic images; development of a method of segmentation of small aerial objects on images obtained from an optical-electronic system; practical verification of the method of segmentation of small aerial objects on optical-electronic images based on the Sobel operator. The methods used are methods of system analysis, mathematical methods of image comparison, methods of digital image processing, methods of discrete mathematics, probability theory, mathematical apparatus of matrix theory, and methods of analytical geometry. The following results are obtained. The features of the images obtained from optical-electronic systems when searching for a multi-dimensional aerial object are considered, and the segmentation methods that allow the detection of the object of interest on the optical-electronic images are analyzed. It has been established that it is necessary to use image segmentation methods that are easy to implement and calculate. It is proposed that segmentation be carried out using a method based on the Sobel edge detector. The proposed method includes two successive stages. This is processing with a Gaussian filter and applying the histogram equalization operation in the first stage, and applying the Sobel edge detector to the results of the first stage in the second. A block diagram of the proposed segmentation method is presented. Experimental studies on the detection of a small aerial object on authentic optical-electronic images have been carried out, and the results of segmentation using the classical Sobel edge detector and the proposed method are given. A visual assessment of the quality of segmentation results using these methods was carried out. Conclusions. A method of segmentation of small aerial objects on optical-electronic images based on the Sobel edge detector has been developed. The direction of further research is to evaluate the quality of segmented images by numerical indicators.
Article Details
References
Lepikh, Ya. I., Santoniy, V. I., Budiyanskaya, L. M., Yanko, V. I. and Balaban, A.P. (2022), “Intelligent electronic-optical sensor for information-measurement system of detection and identification of ground and aerodynamic objects”, Semiconductor Physics, Quantum Electronics & Optoelectronics, vol. 25, no 2, pp. 219–226, doi: https://doi.org/10.15407/spqeo25.02.219
(2024), Beyond FPV – Uses of UAV in Everyday Life, available at: https://www.getfpv.com/learn/fpv-flight-academy/beyond-fpv-uses-of-uav-in-everyday-life/
(2024), FPV drones in Ukraine are changing modern warfare, available at:
Hurin, A., Khudov, H., Kostyria, O., Maslenko, O. and Siadrystyi, S. (2024), “Comparative analysis of spectral anomalies detection methods on images from on-board remote sensing systems”, Advanced Information Systems, vol. 8(2), pp. 48–57, doi: https://doi.org/10.20998/2522-9052.2024.2.06
(2024), Reconnaissance drone, available at: https://www.elp-gmbh.com/en/product-areas/reconnaissance-drone/#:~:text=Reconnaissance%20drones%20are%20classified%20as,autonomous%20from%20a%20safe%20distance
Gonzalez, C. R. and Woods, R. E. (2005), Digital Image Processing. Second Edition, Prentice Hall, Upper Saddle Rever, 977 p., available at: https://www.amazon.com/Digital-Image-Processing-Rafael-Gonzalez/dp/0201180758
Gorokhovatskyi V., Peredrii O., Tvoroshenko I., and Markov T. (2023). “Distance matrix for a set of structural description components as a tool for image classifier creating”, Advanced Information Systems, vol. 7, no. 1, pp. 5–13, doi: https://doi.org/10.20998/2522-9052.2023.1.01
Khudov, H., Makoveichuk , O., Kalimulin, T., Khudov V., and Shamrai N. (2024), “The Method For Approximating The Edge Detection Convolutional Operator Using A Genetic Algorithm For Segmentation Of Complex-Structured Images”, Advanced Information Systems, vol. 8. no. 4. pp. 5–12, doi: https://doi.org/10.20998/2522-9052.2024.4.01
Khudov, H., Makoveichuk, O., Khudov, V., Khizhnyak, I., Kalimulin, T. and Khudov, R. (2023), “The Development of Optical-Electronic Images Segmentation Method Based on the Canny Edge”, IEEE 18th Int. Conf. on Computer Science and Information Technologies (CSIT-2023), October, 19-21, pp. 1–6, doi: https://doi.org/10.1109/CSIT61576.2023.10324149
Kinahan, J. and Smeaton A. F. (2021), “Image Segmentation to Identify Safe Landing Zones for Unmanned Aerial Vehicles”, 29th Irish Conf. on Artificial Intelligence and Cognitive Science AICS'2021, doi: https://doi.org/10.48550/arXiv.2111.14557
Treboux, J. and Genoud, D. (2018), “Improved Machine Learning Methodology for High Precision Agriculture”, IEEE Global Internet of Things Summit (GIoTS), pp, 1–6, doi: https://doi.org/10.1109/GIOTS.2018.8534558
Xiaohai, S., Yan, T., Han, F., Zhida, W. and Xuewu, Z. (2020), “Crop identification using UAV image segmentation. Society of Photo-Optical Instrumentation Engineers”, Second Target Recognition and Artificial Intelligence Summit Forum, 11427. doi: https://doi.org/10.1117/12.2552195
Lin, Z., Doyog, N. D., Huang, S.-F. and C. Lin, (2021),“Segmentation and Classification of UAV-based Orthophoto of Watermelon Field Using Support Vector Machine Technique”, IEEE International Geoscience and Remote Sensing Symposium IGARSS, doi: https://doi.org/10.1109/IGARSS47720.2021.9553715
Khudov, H., Makoveichuk, O., Komarov, V., Khudov, V., Khizhnyak, I., Bashynskyi, V., Stetsiv, S., Dudar, Y., Rudiy, A., and Buhera, M. (2023), “Determination of the number of clusters on images from space optic-electronic observation systems using the k-means algorithm”, Eastern-European Journal of Enterprise Technologies, vol. 3, no. 9(123), pp. 60–69, doi: https://doi.org/10.15587/1729-4061.2023.282374
Khudov, H., Khizhnyak, I., Glukhov, S., Shamrai, N. and Pavlii, V. (2024), “The method for objects detection on satellite imagery based on the firefly algorithm”, Advanced Information Systems, vol. 8, № 1, pp. 5–11, doi: https://doi.org/10.20998/2522-9052.2023.4.01
Ruban, I., Khudov, H., Makoveichuk, O., Khizhnyak, I., Khudov, V. and Shamrai, N. (2023), “The Two Stage Method for Detecting Objects of Interest on Optical Satellite RGB Imagery of the Urban Infrastructure”, 4th Conference KhPI Week on Advanced Technology, October, 02-06, pp. 691–694, doi: https://doi.org/10.1109/KhPIWeek61412.2023.10312970
Petrovska, I., Kuchuk, H. and Mozhaiev, M. (2022), “Features of the distribution of computing resources in cloud systems”, 2022 IEEE 4th KhPI Week on Advanced Technology, KhPI Week 2022 - Conference Proceedings, 03-07 October 2022, Code 183771, doi: https://doi.org/10.1109/KhPIWeek57572.2022.9916459
Sharma, M., Ansari, M. D. and Kumar, R. (2017), “A Comparative Study of Edge Detectors in Digital Image Processing”, IEEE 4th Int. Confe. on Signal Processing, Computing and Control (ISPCC), doi: https://doi.org/10.1109/ISPCC.2017.8269683
Kushnikov V. (2024), “Ukrainian Drone Downs ZALA Lancet Over Sudzha in Kursk Oblast”, Militarnyi, available at: https://mil.in.ua/en/news/
Mohammad, E. J., Taha, R. Y. and Mazher, H. A. (2022), “Design and Fundamentals of Sobel Edge Detection of an Image”, Journal of Multidisciplinary Engineering Science and Technology (JMEST), vol. 9, is. 3, pp.15195–15200, available at: https://www.jmest.org/wp-content/uploads/JMESTN42354017.pdf
Svyrydov, A., Kuchuk, H. and Tsiapa, O. (2018), “Improving efficienty of image recognition process: Approach and case study”, Proceedings of 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018, pp. 593–597, doi: https://doi.org/10.1109/DESSERT.2018.8409201
Kumar, P. (2014), Image Enhancement Using Histogram Equalization and Histogram Specification on Different Color Spaces, Computer Science and Engineering National Institute of Technology Rourkela, India, 29 p., available at: https://core.ac.uk/download/pdf/53189986.pdf