EXPERIMENTAL STUDIES OF THE IMAGE SEGMENTATION METHOD QUALITY FROM UNMANNED AERIAL VEHICLES BASED ON THE ANT COLONY OPTIMIZATION ALGORITHM UNDER THE INFLUENCE OF ADDITIVE GAUSSIAN NOISE
Article Sidebar
Main Article Content
Abstract
The subject matter of the article is experimental studies of the image segmentation method quality from UAVs based on the Ant Colony Optimization algorithm under the influence of additive Gaussian noise. The goal is to reduce the probability of first and second type errors in image segmentation by applying a segmentation method based on the Ant Colony Optimization algorithm under the influence of additive Gaussian noise. The tasks of the study are to evaluate the robustness and accuracy of the image segmentation method based on the Ant Colony Optimization algorithm under varying levels of additive Gaussian noise, and to compare its performance with the classical Sobel filter–based segmentation approach. The methods used are digital image processing techniques, statistical analysis of segmentation quality, implementation of the Ant Colony Optimization algorithm for image segmentation, modeling of noise-contaminated conditions, and comparison of segmentation errors of the first and second kinds. The following results are obtained: the method based on the Ant Colony Optimization algorithm demonstrates superior noise resistance and maintains higher accuracy than the Sobel filter approach. Specifically, it reduces first-kind segmentation errors by 14–23% and second-kind errors by 9–17%, depending on the level of noise. Visual and quantitative analysis confirms the effectiveness of the proposed method in processing UAV-acquired imagery affected by additive Gaussian noise. Conclusions. The experimental findings confirm that the method based on the Ant Colony Optimization algorithm outperforms conventional edge detection techniques, particularly under noisy conditions, providing improved accuracy and robustness across a range of noise intensities.
Article Details
References
Mohsan, S. A. H., Oth Man, N. Q. H., Li, Y., Alsharif, M. H., and Khan, M. A. (2023), “Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends”, Intelligent Service Robotics, vol. 16 (1), pp. 109–137, doi: https://doi.org/10.1007/s11370-022-00452-4
Ahmed, S. A., Desa, H., Easa, H. K., Hussain, A.-S. T., Taha, T. A., Salih, S. Q., Hasan, R. A., Ahmed, O. K. and Ng, P. S. J. (2024), “Advancements in UAV image semantic segmentation: A comprehensive literature review”, Multidisciplinary Reviews, vol. 7 (6), e2024118, doi: https://doi.org/10.31893/multirev.2024118
Liu, Z., An, P., Yang, Y., Qiu, S., Liu, Q., and Xu, X. (2024), “Vision-Based Drone Detection in Complex Environments: A Survey”, Drones, vol. 8(11), 643, doi: https://doi.org/10.3390/drones8110643
Motlagh, N. H., Kortoci, P., Su, X., Loven, L., Hoel, H. K., Haugsvaer, S. B., Srivastava, V., Gulbrandsen, C., and Tarkoma, S. (2023), “Unmanned Aerial Vehicles for Air Pollution Monitoring: A Survey”, IEEE Internet of Things Journal, vol. 10(24), pp. 21687–21704, doi: https://doi.org/10.1109/JIOT.2023.3290508
Rascon, C., and Martinez-Carranza, J. (2024), “A Review of Noise Production and Mitigation in UAVs”, Machine Learning for Complex and Unmanned Systems, pp. 220–235, CRC Press, doi: https://doi.org/10.1201/9781003385615-12
Kryvenko S., Lukin V., Bondzulic B., and Stojanovic N. (2025), “Compression of noisygrayscaleimageswith compression ratio analysis”, Advanced Information Systems, vol. 9. no 2, pp. 68–74, doi: https://doi.org/10.20998/2522-9052.2025.2.09
Zhang, Z., and Zhu, L.-X. (2023), “A Review on Unmanned Aerial Vehicle Remote Sensing: Platforms, Sensors, Data Processing Methods, and Applications”, Drones, vol. 7(6), 398, doi: https://doi.org/10.3390/drones7060398
Li, Q., Zhang, Y., Fang, L., Kang, Y., Li, S., and Zhu, X. X. (2024), “DREB-Net: Dual-stream Restoration Embedding Blur-feature Fusion Network for High-mobility UAV Object Detection”, IEEE Transactions on Geoscience and Remote Sensing, vol. 63, doi: https://doi.org/10.1109/TGRS.2025.3543270
Wang, X., Peng, Y., and Shen, C. (2025), “Efficient feature fusion for UAV object detection”, IEEE Transactions on Image Processing, vol. 34, pp. 123–135, doi: https://doi.org/10.1109/TIP.2025.1234567
Li, C., Zhao, R., Wang, Z., Xu, H., and Zhu, X. (2024), “RemDet: Rethinking Efficient Model Design for UAV Object Detection”, arXiv preprint arXiv:2412.10040, doi: https://doi.org/10.48550/arXiv.2412.10040
Zhang, Z., and Li, G. (2025), “UAV imagery real-time semantic segmentation with global–local information attention”, Sensors, vol. 25(6), pp. 1786, doi: https://doi.org/10.3390/s25061786
Ding, R. (2024), “Segmentation analysis of UAV images based on Unet deep learning algorithm”, Applied and Computational Engineering, vol. 54, pp. 248–253, doi: https://doi.org/10.54254/2755-2721/54/20241644
Khudov, H., Khudov, V., Makoveichuk, O., Khizhnyak, I., Hridasov, I., Butko, I., Khudov, R., Glukhov, S., Shamrai, N., and Lisohorskyi, B. (2025), “Development of an image segmentation method from unmanned aerial vehicles based on the particle swarm optimization algorithm”, Technology Audit and Production Reserves, № 3/2(83), pp. 88–95, doi: https://doi.org/10.15587/2706-5448.2025.330973
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, no. 1, pp. 5–11, doi: https://doi.org/10.20998/2522-9052.2023.4.01
Khudov, H., Hridasov, I., Khizhnyak, I., Yuzova, I., and Solomonenko, Y. (2024), “Segmentation of image from a first-person-view unmanned aerial vehicle based on a simple ant algorithm”, Eastern-European Journal of Enterprise Technologies, vol. 4(9 (130)), pp, 44–55, doi: https://doi.org/10.15587/1729-4061.2024.310372
Khudov, H., Koval, V., Khizhnyak, I., Bridnia, Y., Chepurnyi, V., Prykhodko, S., Oleksenko, O., Glukhov, S. and Savran, V. (2022), “The method of transport logistics problem solving by the MAX-MIN ACO algorithm”, International Journal of Emerging Technology and Advanced Engineering, vol. 12 (07), pp.108–119, doi: https://doi.org/10.46338/ijetae0722_12
Zhao, H.-Y., Wang, J.-C., Guan, X., Wang, Z.-H., He, Y.-H. and Xie, H.-L. (2020), “Ant Colony System for Energy Consumption Optimization in Mobile IoT Networks”, Journal of Circuits Systems and Computers, vol. 29(9), 2050150, doi: https://doi.org/10.1142/S0218126620501509
Dorigo, M., Maniezzo, V., and Colorni, A. (1996), “Ant system: Optimization by a colony of cooperating agents”, IEEE Trans. on Systems, Man, and Cybernetics – Part B, vol. 26(1), pp. 29–41. doi: https://doi.org/10.1109/3477.484436
Kuchuk, H., Mozhaiev, O., Tiulieniev, S., Mozhaiev, M., Kuchuk, N., Tymoshchyk, L., Lubentsov, A., Onishchenko, Y., Gnusov, Y. and Tsuranov, M. (2025), “Devising a method for increasing data transmission speed in monitoring systems based on the mobile high-density Internet of Things”, Eastern-European Journal of Enterprise Technologies, vol. 3(4 (135)), pp. 52–61, doi: https://doi.org/10.15587/1729-4061.2025.330644
Petrovska, I., Kuchuk, H., Kuchuk, N., Mozhaiev, O., Pochebut, M. and Onishchenko, Yu. (2023), “Sequential Series-Based Prediction Model in Adaptive Cloud Resource Allocation for Data Processing and Security”, 2023 13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023, 13–15 October, Athens, Greece, code 197136, doi: https://doi.org/10.1109/DESSERT61349.2023.10416496
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
Khudov, H., Khudov, R., Khizhnyak, I., Hridasov, I., and Hlushchenko, P. (2025), “The small aerial objects segmentation method on optical-electronic images based on the Sobel Edge Detector”, Advanced Information Systems, vol. 9, no. 2, pp. 5–10, doi: https://doi.org/10.20998/2522-9052.2025.2.01
(2026), Portalgis.pro – industry information resource, available at: https://portalgis.pro/bpla/bezkoshtovni-resursy-bpla
Khudov, H., Makoveichuk, O., Khizhnyak, I., Glukhov, S.,, Shamrai, N., Rudnichenko, S., Husak, M., and Khudov, R, (2022), “The Choice of Quality Indicator for the Image Segmentation Evaluation”, International Journal of Emerging Technology and Advanced Engineering, vol. 12 (10), pp. 95–103, doi: https://doi.org/10.46338/ijetae1022_11
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
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, no. 2, pp. 48–57, doi: https://doi.org/10.20998/2522-9052.2024.2.06