Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-25T04:43:09.106Z Has data issue: false hasContentIssue false
  • English
  • Français

A new Concentric Circles Detection method for Object Detection applied to Radar Images

Published online by Cambridge University Press:  27 February 2019

José Miguel Guerrero Open the ORCID record for José Miguel Guerrero [Opens in a new window] ,
Andreas Muñoz ,
Matilde Santos  and
Gonzalo Pajares
José Miguel Guerrero*
Affiliation:
(Complutense University of Madrid, Computer Science Faculty, Spain)
Andreas Muñoz
Affiliation:
(CIRCE Foundation (Research Centre for Energy Resources and Consumption))
Matilde Santos
Affiliation:
(Complutense University of Madrid, Computer Science Faculty, Spain)
Gonzalo Pajares
Affiliation:
(Complutense University of Madrid, Computer Science Faculty, Spain)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

In this work, a new concentric circles detection method for object detection is proposed. It has been applied to the images of a commercial radar, captured with a Charge-Coupled Device (CCD) camera. The processing includes the detection of centres and concentric circles in the images and the identification of the radar scale. Several methods found in the literature have been applied and compared with our novel proposal for multiple concentric circles detection, called “Propagation Method based on Circular Regression”. This methodology has been validated with real radar images, proving its efficiency in obtaining the distance of any object to a marine vessel, with high accuracy and low computational cost, in real time. This system can not only be applied to most existing radars in the market by adjusting the parameters of each model but our proposal for concentric circle detection can be also applied to other sensing applications.

Keywords

Obstacle detectionRadarImage processingCircles detectionReal-time alarm system
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 4 , July 2019 , pp. 1070 - 1088
Copyright
Copyright © The Royal Institute of Navigation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Akinlar, C., Topal, C. (2013). EDCircles: a real-time circle detector with a false detection control. Pattern Recognition, 46, 725–740.10.1016/j.patcog.2012.09.020Google Scholar
Almeida, C., Franco, T., Ferreira, H., Martins, A., Santos, R., Almeida, J.M., Carvalho, J., Silva, E. (2009). Radar based collision detection developments on USV ROAZ II. OCEANS 2009-EUROPE, Bremen, Germany, 1–6.Google Scholar
Ayala-Ramirez, V., Garcia-Capulin, C., Perez-Garcia, A., Sanchez-Yanez, R. (2006). Circle detection on images using genetic algorithms. Pattern Recognition Letters, 27, 652–657.10.1016/j.patrec.2005.10.003Google Scholar
Blaich, M., Köhler, S., Schuster, M., Schuchhardt, T., Reuter, J. and Tietz, T. (2015). Mission integrated collision avoidance for USVs using laser range finder. OCEANS 2015-Genova, 1–6.Google Scholar
Bole, A., Wall, A. and Norris, A. (2014). Radar and ARPA Manual: Radar, AIS and Target Tracking for Marine Radar Users, Butterworth-Heinemann.Google Scholar
Bone, P. (2010). Hough Transform for circle detection. http://www.mathworks.com/matlabcentral/fileexchange/9833-hough-transform-for-circle-detection. Accessed 21 December 2018.Google Scholar
Bonin-Font, F., Gomila, C. C. and Codina, G. O. (2017). Towards visual navigation of an autonomous underwater vehicle in areas with Posidonia Oceanica. Revista Iberoamericana de Automática e Informática Industrial RIAI, 15(1), 24–35.10.4995/riai.2017.8828Google Scholar
Bovcon, B., Perš, J. and Kristan, M. (2018). Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation. Robotics and Autonomous Systems, 104, 1–13.10.1016/j.robot.2018.02.017Google Scholar
Briechle, K. and Hanebeck, U. D. (2001). Template matching using fast normalized cross correlation. Proceedings of Optical Pattern Recognition XII Volume 4387.10.1117/12.421129Google Scholar
Cao, X. and Deravi, F. (1992). An efficient method for the detection of multiple concentric circles. Proceedings of ICASSP-92:1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, San Francisco, CA, Vol 3, 137–140.10.1109/ICASSP.1992.226257Google Scholar
Chaves, D., Saikia, S., Fernandez-Robles, L., Alegre, E. and Trujillo, M. (2018). A systematic review on object localisation methods in images. Revista Iberoamericana de Automática e Informatica Industrial, 15(3), 231–242.10.4995/riai.2018.10229Google Scholar
Chen, X., Lu, L. and Gao, Y. (2012). A new concentric circle detection method based on Hough transform. Proceedings of 7th International Conference on Computer Science & Education (ICCSE), Melbourne, VIC, 753–758.10.1109/ICCSE.2012.6295182Google Scholar
Cuevas, E., Zaldiwar, D., Perez-Cisneros, M. and Ramirez-Ortegon, M. (2010). Circle detection using discrete differential evolution optimization. Pattern Analysis Applications, 14, 93–107.10.1007/s10044-010-0183-9Google Scholar
Cuevas, E., Osuna, V. and Oliva, D. (2017). Evolutionary Computation Techniques: A Comparative Perspective. Springer International Publishing. 35–65.10.1007/978-3-319-51109-2_3Google Scholar
Frosio, I. and Borghese, N. (2008). Real-time accurate circle fitting with occlusions. Pattern Recognition, 41, 1041–1055.10.1016/j.patcog.2007.08.011Google Scholar
FURUNO 1715. (2018). https://www.furunousa.com/en/products/1715. Accessed 21 December 2018.Google Scholar
Gupta, S. and Singh, Y. J. (2014). Object detection using shape features. IEEE International Conference Computational Intelligence and Computing Research, Coimbatore, 1–4.10.1109/ICCIC.2014.7238445Google Scholar
Halterman, R. and Brush, M. (2010). Velodyne HDL-64E LIDAR for unmanned surface vehicle obstacle detection. Proceedings of SPIE–The International Society for Optical Engineering, Orlando, United States, 76920D-1-8.10.1117/12.850611Google Scholar
Han, J., Koczy, L. and Poston, T. (1993). Fuzzy Hough transform. Proceedings of the 2nd International Conference on Fuzzy Systems, San Francisco, CA, vol. 2, pp. 803–808.10.1109/FUZZY.1993.327545Google Scholar
Heidarsson, H. and Sukhatme, G. (2011). Obstacle detection and avoidance for an autonomous surface vehicle using a profiling sonar. IEEE International Conference on Robotics and Automation, Shanghai, pp. 731–736.10.1109/ICRA.2011.5980509Google Scholar
Hough, P. (1962). A method and means for recognizing complex patterns. U.S. Patent Office No 306954.Google Scholar
Illingworth, J. and Kittler, J. (1988). A survey of the Hough transform. Computer vision, graphics, and image processing, 44(1), 87–116.10.1016/S0734-189X(88)80033-1Google Scholar
Jiang, G. and Quan, L. (2005). Detection of concentric circles for Camera Calibration. Proceedings of Tenth IEEE International Conference on Computer Vision (ICCV'05), 1, 333–340.10.1109/ICCV.2005.73Google Scholar
Kanan, C. and Cottrell, G. W. (2012). Color-to-grayscale: does the method matter in image recognition? PloS ONE, 7(1), e29740.10.1371/journal.pone.0029740Google Scholar
Kapadia, A. S., Chan, W. and Moyé, L. A. (2017). Mathematical statistics with applications. Boca Raton: CRC Press.10.1201/9781315275864Google Scholar
Lee, S., Kwon, K. and Joh, J. (2004). A fuzzy logic for autonomous navigation of marine vehicles satisfying COLREG guidelines. International Journal of Control, Automation and Systems, 2(2), 171–181.Google Scholar
Liu, D., Wang, Y., Tang, Z. and Lu, X. (2014). A robust circle detection algorithm based on top-down least-square fitting analysis. Computers and Electrical Engineering, 40 1415–1428.10.1016/j.compeleceng.2014.03.011Google Scholar
Logitech. (2018). Logitech C920 Webcam. https://www.logitech.com/es-es/product/hd-pro-webcam-c920. Accessed 21 December 2018.Google Scholar
Muammar, H. and Nixon, M. (1989). Approaches to extending the Hough transform. Proceedings of the International Conference on Acoustics, Speech and Signal Processing ICASSP-89, 1556–1559.10.1109/ICASSP.1989.266739Google Scholar
Menoyo, J. and Santos, M. (2016). Intelligent rudder control of an unmanned surface vessel. Expert Systems with Applications, 55, 106–117.Google Scholar
Nao, Q., Ye, Y., Mo, C., Wu, Y. and Liu, L. (2010). Method for the Detection of Concentric Circles of Photoelectric Image of Circular Hole in Printed Circuit Board. Acta Photonica Sinica, 30, 75–78.Google Scholar
Oleynikova, E., Lee, N. B., Barry, A. J. and Holler, J. (2010). Perimeter patrol on autonomous surface vehicles using marine radar. OCEANS, Sydney, Australia, 1–5.Google Scholar
Onunka, C. and Bright, G. (2010). Autonomous marine craft navigation: On the study of radar obstacle detection. Proceedings of 11th International Conference on Control Automation Robotics & Vision, Singapore, 567–572.10.1109/ICARCV.2010.5707239Google Scholar
OpenCV. (2018). https://opencv.org/. Accessed 21 December 2018.Google Scholar
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62–66.10.1109/TSMC.1979.4310076Google Scholar
Pajares, G. and de la Cruz, J.M. (2007). Visión por Computador: Imágenes Digitales y Aplicaciones, RA-MA, Madrid.Google Scholar
Polvara, R., Sharma, S., Wan, J., Manning, A. and Sutton, R. (2018). Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles. The Journal of Navigation, 71(1), 241–256.10.1017/S0373463317000753Google Scholar
Raspberry Pi Model B. (2018). https://www.raspberrypi.org/products/raspberry-pi-2-model-b/. Accessed 21 December 2018.Google Scholar
Roth, G. and Levine, M. (1994). Geometric primitive extraction using a genetic algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 901–905.10.1109/34.310686Google Scholar
Shaked, D., Yaron, O. and Kiryati, N. (1996). Deriving stopping rules for the probabilistic Hough transform by sequential analysis. Computer Vision Image Understanding, 63, 512–526.10.1006/cviu.1996.0038Google Scholar
Silveira, M. (2005). An algorithm for the detection of multiple concentric circles. Pattern Recognition and Image Analysis, 3523, 143–224.Google Scholar
Xu, L., Oja, E. and Kultanen, P. (1990). A new curve detection method: randomized Hough transform (RHT). Pattern Recognition Letters, 11, 331–338.10.1016/0167-8655(90)90042-ZGoogle Scholar
Yang, C., Park, J. and Rashid, A. (2018). An improved method of land masking for synthetic aperture radar-based ship detection. Journal of Navigation, 71(4), 788–804.10.1017/S037346331800005XGoogle Scholar
Yuen, H. K., Princen, J., Illingworth, J. and Kittler, J. (1990). Comparative study of Hough transform methods for circle finding. Image and vision computing, 8(1), 71–77.10.1016/0262-8856(90)90059-EGoogle Scholar
Zhuang, J., Zhang, L., Zhao, S., Cao, J., Wang, B. and Sun, H. (2016). Radar-based collision avoidance for unmanned surface vehicles. China Ocean Engineering, 30(6), 867–883.10.1007/s13344-016-0056-0Google Scholar