Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
권호 표지
DOI QR코드

DOI QR Code

Real-time Small Target Detection using Local Contrast Difference Measure at Predictive Candidate Region

예측 후보 영역에서의 지역적 대비 차 계산 방법을 활용한 실시간 소형 표적 검출

  • 반종희 (대구대학교 정보통신공학과) ;
  • 왕지현 (국방과학연구소 신개념무기팀) ;
  • 이동화 (대구대학교 정보통신공학과) ;
  • 유준혁 (대구대학교 정보통신공학과) ;
  • 유성은 (대구대학교 정보통신공학과)
  • Received : 2016.12.05
  • Accepted : 2017.01.06
  • Published : 2017.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

In This Paper, we find the Target Candidate Region and the Location of the Candidate Region by Performing the Morphological Difference Calculation and Pixel Labeling for Robust Small Target Detection in Infrared Image with low SNR. Conventional Target Detection Methods based on Morphology Algorithms are low in Detection Accuracy due to their Vulnerability to Clutter in Infrared Images. To Address the Problem, Target Signal Enhancement and Background Clutter Suppression are Achieved Simultaneously by Combining Moravec Algorithm and LCM (Local Contrast Measure) Algorithm to Classify the Target and Noise in the Candidate Region. In Addition, the Proposed Algorithm can Efficiently Detect Multiple Targets by Solving the Problem of Limited Detection of a Single Target in the Target Detection method using the Morphology Operation and the Gaussian Distance Function Which were Developed for Real time Target Detection.

본 논문에서는 낮은 SNR을 가지는 적외선 영상에서 강인한 소형 표적 검출을 위해 모폴로지 차 연산을 수행하여 표적 후보 영역을 찾고 화소 라벨링을 통해 후보 영역의 위치를 찾는다. 기존의 모폴로지 연산 기반의 표적 검출 방법들은 적외선 영상에 존재하는 클러터에 취약하다는 단점으로 인해 검출 정확도가 낮다. 이러한 문제를 해결하기 위해 본 논문에서는 후보 영역에서 표적과 배경 잡음을 분류하기 위해 Moravec 알고리즘과 LCM(Local Contrast Measure) 알고리즘을 결합함으로써 표적 향상과 배경 잡음 억제를 동시에 달성한다. 또한, 제안하는 알고리즘은 기존에 실시간 표적 검출을 위해 개발되었던 모폴로지 연산과 가우시안 거리 함수를 이용한 표적 검출 방법의 단일 객체에 제한적인 검출 문제를 해결하여 복수 객체를 효율적으로 검출할 수 있다.

Keywords

References

  1. Jong, A. D., "IRST and its Perspective", SPIE's International Symposium on Optical Science, Engineering, and Instrumentation. International Society for Optics and Photonics, Vol. 2552, pp. 206-213, 1995.
  2. Zhang, Z., Li, C. and Shi, L., "Detecting and Tracking Dim Moving Point Target in IR Image Sequence", Infrared Physics & Technology, Vol. 46, No. 4, pp. 323-328, 2005. https://doi.org/10.1016/j.infrared.2004.06.001
  3. Deshpande, S. D., Er, M. H., Ronda, V. and Chan, P., "Max-mean and Max-median Filters for Detection of Small Targets", SPIE's International Symposium on Optical Science, Engineering, and Instrumentation. International Society for Optics and Photonics, Vol. 3809, pp. 74-83, 1999.
  4. Boccignone, G., Chianese, A. and Picariello, A., "Small Target Detection Using Wavelets", In: Pattern Recognition. Proceedings. Fourteenth International Conference on. IEEE, Vol. 2, pp. 1776-1778, 1998.
  5. Park, J. J., Ahn, S. H., Kim, J. H., Kim, S. K., "Small Target Detection using Morphology and Gaussian Distance Function in Infrared Images", Journal of the Korea Industrial Information Systems Research, Vol. 17, No. 4, pp.61-70, 2012. https://doi.org/10.9723/JKSIIS.2012.17.4.061
  6. Kim, J. H., Park, J. J., Ahn, S. H., Lee, D. G., Moon, D. S., Kim, S. K., "Small target Detection using Morphology and Modified Gaussian Distance Function", Published Online in Wiley Online Library. Security and Communication networks, 2014.
  7. Son, J. M., Ahn, S. H., Kim, J. H., Kim, S. K., "Improvement of Detecting Speed of Small Target Using SAD Algorithm", Journal of the Korea Industrial Information Systems Research, Vol. 18, No. 4, pp. 53-60, 2013. https://doi.org/10.9723/jksiis.2013.18.4.053
  8. Mao, X. and Diao, W., "Criterion to Evaluate the Quality of Infrared Small Target Images", Journal of Infrared. Millimeter. and Terahertz Waves, Vol. 30, No. 1, pp. 56-64, 2009. https://doi.org/10.1007/s10762-008-9410-5
  9. Kim, S., "Min-local-log Filter for Detecting Small Targets in Cluttered Background", Electronics Letters, Vol. 47, No. 2, pp. 105-106, 2011. https://doi.org/10.1049/el.2010.2066
  10. Chen, Philip, CL., et al. "A Local Contrast Method for Small Infrared Target Detection", IEEE Transactions on Geoscience and Remote Sensing, Vol. 52. No. 1, pp. 574-581, 2014. https://doi.org/10.1109/TGRS.2013.2242477
  11. Harris, Chris, and Stephens, M., "A Combined Corner and Edge Detector", Alvey Vision Conference. Vol. 15. 1988.
  12. Seo, B. H., Kim, B. M., Moon, C. B., Shin, Y. S., "Binarization of Number Plate Image with a Shadow", Journal of the Korea Industrial Information Systems Research, Vol. 13, No. 4, pp. 1-13, 2008.
  13. Otsu, N., "A Threshold Selection Method from Gray-Level Histogram", IEEE Transactions on Systems. Man. And Cybernetics, Vol. 9, pp. 62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076
  14. Hilliard, C. I., "Selection of a Clutter Rejection Algorithm for Real-Time Target Detection from an Airborne Platform", AeroSense 2000. International Society for Optics and Photonics, Vol. 4048, pp. 74-84, 2000.
  15. Qin, Hanlin, et al. "Multiscale Random Projection Based Background Suppression of Infrared Small Target Image." Infrared Physics & Technology, Vol. 73, pp. 255-262, 2015. https://doi.org/10.1016/j.infrared.2015.09.016
  16. "Incredible UFO filmed in Infrared!! March 23, 2013 ", https://www.youtube.com/watch?v=VU0jL6L2Zc4, 2013.
  17. "Six Clips Of UFOs Over East Texas July 7 2009", https://www.youtube.com/watch?v=cGd1-EEgTJE, 2009.
  18. "Innovative Research: Delta Waterfowl Uses Drones and Thermal Imaging to Locate Nesting Ducks", https://www.youtube.com/watch?v=ZWpgIvikHrU, 2016.
  19. Kim, S. Y. and Lee, S. M., "Implementation of an Image Board Remote Control System Using PDA Based on Embedded Linux in Wireless Internet," The Journal of Information Systems, Vol. 17, No. 1, pp. 155-171, 2008. https://doi.org/10.5859/KAIS.2008.17.1.155
  20. Kim, S. Y., Yoon, C. Y. and Yu, E. J., "A Study on the Development of Learning Contents of Augmented Reality by Perception Rate and Speeding," The Journal of Internet Electronic Commerce Research, Vol. 14, No. 4, pp. 313-333, 2014.