Devising an image processing method for transport infrastructure monitoring systems
- 1. International Research and Training Center for Information Technologies and Systems of the National Academy of Sciences (NAS) of Ukraine and Ministry of Education and Science (MES) of Ukraine
Description
Identifying and categorizing contours in images is important in many areas of computer vision. Examples include such operational tasks solved by using unmanned aerial vehicles as dynamic monitoring of the condition of transport infrastructure, in particular road markings.
This study has established that current methods of image contour analysis do not produce clear and reliable results when solving the task of monitoring the state of road markings. Therefore, it is a relevant scientific and applied task to improve the methods and models of filtration, processing of binary images, and qualitative and meaningful separation of the boundaries of objects of interest.
To solve the task of highlighting road marking contours on images acquired from an unmanned aerial vehicle, a method has been devised that includes an operational tool for image preprocessing – a combined filter. The method has several advantages and eliminates the limitations of known methods in determining the boundaries of the location of the object of interest, by highlighting the contours of a cluster of points using histograms.
The method and procedures reported here make it possible to successfully solve problems that are largely similar to those that an expert person can face when solving intelligent tasks of processing and filtering information.
The proposed method was verified at an enterprise producing the Ukrainian unmanned aerial vehicle "Spectator" during tests of information technology of dynamic monitoring of the state of transport infrastructure.
The results could be implemented in promising intelligent control systems in the field of modeling human conscious behavior when sorting data required for the perception of environmental features
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Devising an image processing method for transport infrastructure monitoring systems.pdf
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References
- Kozub, A. N., Suvorova, N. A., Chernjavskiy, V. N. (2011). Analysis of the means of gathering information for geographic information systems. Systemy ozbroiennia i viyskova tekhnika, 3 (27), 42–47. Available at: http://www.hups.mil.gov.ua/periodic-app/article/1876/soivt_2011_3_12.pdf
- Berezina, S. I., Blinichkin, K. V. (2014). Automation of data rejection process obtained from unmanned aerial vehicles (UAVs). Nauka i tekhnika Povitrianykh Syl Zbroinykh Syl Ukrainy, 1 (14), 82–89. Available at: http://irbis-nbuv.gov.ua/cgi-bin/irbis_nbuv/cgiirbis_64.exe?C21COM=2&I21DBN=UJRN&P21DBN=UJRN&IMAGE_FILE_DOWNLOAD=1&Image_file_name=PDF/Nitps_2014_1_20.pdf
- Weiss, M., Baret, F. (2017). Using 3D Point Clouds Derived from UAV RGB Imagery to Describe Vineyard 3D Macro-Structure. Remote Sensing, 9 (2), 111. doi: https://doi.org/10.3390/rs9020111
- Li, C., Miller, J., Wang, J., Koley, S. S., Katz, J. (2017). Size Distribution and Dispersion of Droplets Generated by Impingement of Breaking Waves on Oil Slicks. Journal of Geophysical Research: Oceans, 122 (10), 7938–7957. doi: https://doi.org/10.1002/2017jc013193
- Gritsenko, V., Volkov, O., Bogachuk, Y., Gospodarchuk, O., Komar, M., Shepetukha, Y., Volosheniuk, D. (2020). Intellectual Control, Localization and Mapping in Geographic Information Systems Based on Analysis of Visual Data. Cybernetics and Computer Engineering, 2020 (2 (200)), 41–58. doi: https://doi.org/10.15407/kvt200.02.041
- Volkov, O., Bogachuk, Yu., Komar, M., Voloshenyuk, D. (2020). Two-level technology of intelligent application of on-board video camera of unmanned aerial vehicle for monitoring of geospatial data. Science-Based Technologies, 47 (3), 329–341. doi: https://doi.org/10.18372/2310-5461.47.14873
- Barrile, V., Meduri, G. M., Critelli, M., Bilotta, G. (2017). MMS and GIS for Self-driving Car and Road Management. Lecture Notes in Computer Science, 68–80. doi: https://doi.org/10.1007/978-3-319-62401-3_6
- Liu, W., Zhang, Z., Li, S., Tao, D. (2017). Road Detection by Using a Generalized Hough Transform. Remote Sensing, 9 (6), 590. doi: https://doi.org/10.3390/rs9060590
- Mukhopadhyay, P., Chaudhuri, B. B. (2015). A survey of Hough Transform. Pattern Recognition, 48 (3), 993–1010. doi: https://doi.org/10.1016/j.patcog.2014.08.027
- Marzougui, M., Alasiry, A., Kortli, Y., Baili, J. (2020). A Lane Tracking Method Based on Progressive Probabilistic Hough Transform. IEEE Access, 8, 84893–84905. doi: https://doi.org/10.1109/access.2020.2991930
- Mammeri, A., Boukerche, A., Tang, Z. (2016). A real-time lane marking localization, tracking and communication system. Computer Communications, 73, 132–143. doi: https://doi.org/10.1016/j.comcom.2015.08.010
- Yan, X., Li, Y. (2017). A method of lane edge detection based on Canny algorithm. 2017 Chinese Automation Congress (CAC). doi: https://doi.org/10.1109/cac.2017.8243122
- Lee, C., Moon, J.-H. (2018). Robust Lane Detection and Tracking for Real-Time Applications. IEEE Transactions on Intelligent Transportation Systems, 19 (12), 4043–4048. doi: https://doi.org/10.1109/tits.2018.2791572
- Bouganssa, I., Sbihi, M., Zaim, M. (2019). Laplacian Edge Detection Algorithm for Road Signal Images and FPGA Implementation. International Journal of Machine Learning and Computing, 9 (1), 57–61. doi: https://doi.org/10.18178/ijmlc.2019.9.1.765
- AbdelAty, A. M., Elwakil, A. S., Radwan, A. G., Psychalinos, C., Maundy, B. J. (2018). Approximation of the Fractional-Order Laplacian sα As a Weighted Sum of First-Order High-Pass Filters. IEEE Transactions on Circuits and Systems II: Express Briefs, 65 (8), 1114–1118. doi: https://doi.org/10.1109/tcsii.2018.2808949
- Chaple, G. N., Daruwala, R. D., Gofane, M. S. (2015). Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA. 2015 International Conference on Technologies for Sustainable Development (ICTSD). doi: https://doi.org/10.1109/ictsd.2015.7095920
- Ozgunalp, U. (2017). Combination of the symmetrical local threshold and the sobel edge detector for lane feature extraction. 2017 9th International Conference on Computational Intelligence and Communication Networks (CICN). doi: https://doi.org/10.1109/cicn.2017.8319349
- Dinakaran, K., Stephen Sagayaraj, A., Kabilesh, S. K., Mani, T., Anandkumar, A., Chandrasekaran, G. (2021). Advanced lane detection technique for structural highway based on computer vision algorithm. Materials Today: Proceedings, 45, 2073–2081. doi: https://doi.org/10.1016/j.matpr.2020.09.605
- Feng, Y., Rong-ben, W., Rong-hui, Z. (2008). Research on Road Recognition Algorithm Based on Structure Environment for ITS. 2008 ISECS International Colloquium on Computing, Communication, Control, and Management. doi: https://doi.org/10.1109/cccm.2008.362
- Syniavskyi, V. (2019). Analysis of image segmentation methods. Materialy VII naukovo-tekhnichnoi konferentsiyi «Informatsiyni modeli, systemy ta tekhnolohiyi». Ternopil, 171. Available at: http://elartu.tntu.edu.ua/bitstream/lib/30369/2/IMST_2019_Syniavskyi_V-Analysis_of_image_segmentation_171.pdf
- Danyk, Y., Protcenko, М. (2013). Choice of color model for the digital processing of images in unmanned aircraft system. Visnyk ZhDTU, 2 (65), 43–49. Available at: http://eztuir.ztu.edu.ua/bitstream/handle/123456789/2602/8.pdf?sequence=1&isAllowed=y
- Roushdy, M. (2006). Comparative study of edge detection algorithms applying on the grayscale noisy image using morphological filter. GVIP Journal, 6 (4), 17–23. Available at: https://www.researchgate.net/publication/229014057_Comparative_study_of_edge_detection_algorithms_applying_on_the_grayscale_noisy_image_using_morphological_filter
- Muthalagu, R., Bolimera, A., Kalaichelvi, V. (2020). Lane detection technique based on perspective transformation and histogram analysis for self-driving cars. Computers & Electrical Engineering, 85, 106653. doi: https://doi.org/10.1016/j.compeleceng.2020.106653