Journal of the Korean Wood Science and Technology

The Korean Society of Wood Science & Technology (한국목재공학회)
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The Development of Image Processing System Using Area Camera for Feeding Lumber

영역카메라를 이용한 이송중인 제재목의 화상처리시스템 개발

  • Kim, Byung Nam (Division of Wood Engineering, Department of Forest Products, Korea Forest Research Institute) ;
  • Lee, Hyoung Woo (College of Agriculture & Life Science, Chonnam National University) ;
  • Kim, Kwang Mo (Division of Wood Engineering, Department of Forest Products, Korea Forest Research Institute)
  • 김병남 (국립산림과학원 임산공학부 목재성능과) ;
  • 이형우 (전남대학교 농업생명과학대학) ;
  • 김광모 (국립산림과학원 임산공학부 목재성능과)
  • Received : 2008.09.02
  • Accepted : 2008.10.20
  • Published : 2009.01.25
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Abstract

For the inspection of wood, machine vision is the most common automated inspection method used at present. It is required to sort wood products by grade and to locate surface defects prior to cut-up. Many different sensing methods have been applied to inspection of wood including optical, ultrasonic, X-ray sensing in the wood industry. Nowadays the scanning system mainly employs CCD line-scan camera to meet the needs of accurate detection of lumber defects and real-time image processing. But this system needs exact feeding system and low deviation of lumber thickness. In this study low cost CCD area sensor was used for the development of image processing system for lumber being fed. When domestic red pine being fed on the conveyer belt, lumber images of irregular term of captured area were acquired because belt conveyor slipped between belt and roller. To overcome incorrect image merging by the unstable feeding speed of belt conveyor, it was applied template matching algorithm which was a measure of the similarity between the pattern of current image and the next one. Feeding the lumber over 13.8 m/min, general area sensor generates unreadable image pattern by the motion blur. The red channel of RGB filter showed a good performance for removing background of the green conveyor belt from merged image. Threshold value reduction method that was a image-based thresholding algorithm performed well for knot detection.

최근 목재산업계에서는 사람의 시각을 대체하는 기계시각을 이용한 화상처리시스템을 도입하여 제재목 등급 판정의 자동화, 제품의 품질향상 및 재단 최적화 등에 활용하고 있다. 본 연구에서는 국내산 소나무 제재목을 대상으로 표면결함검출을 위한 화상처리시스템을 개발하고자 하였으며, 주로 이용되고 있는 라인스캔카메라를 대신하여 비교적 저가의 영역카메라를 이용하였을 때 발생되는 문제점을 해결하고자 하였다. 벨트컨베이어의 불균일한 이송속도에 따른 문제점을 해결하기 위해 화상의 특징점을 이용한 결과 효과적인 화상병합을 할 수 있었다. 일반적인 영역카메라는 송재속도 15.7 m/min 이상에서는 모션블러에 의한 화상의 품질저하로 인하여 화상처리가 어려웠고 화상처리에 적합한 송재속도는 13.8 m/min였으며 추후 송재속도를 향상시키기 위해서는 전자셔터 속도가 빠른 카메라의 사용이 요구되었다. 녹색 컨베이어벨트상의 제재목 화상의 배경과의 분리를 위해서는 RGB필터의 red 채널을 이용하면 효과적이었다. 옹이검출을 위한 문턱값 판정법은 화상분석형인 문턱값 감소법이 우수하였으며 히스토그램분석형 중에서는 엔트로피법이 적합하였다.

Keywords

References

  1. Huber, H. A., C. W. McMillin, and McKinney. 1985. Lumber defect detection abilities of furniture rough mill employees. Forest Prod. J. 35(11/12): 79-82.
  2. McMillin, C. W., R. W. Conners, and H. A. Huber. 1984. ALPS-A potential new automated lumber processing system. Forest Products Journal 34(1):13-20.
  3. 김병남. 2006. 제재 및 재단공정 최적화를 위한 목재의 형상인식기술 개발. 전남대학교 박사학위논문.
  4. Funck, J. W., Y. Zhong, D. A. Butler, C. C. Brunner, and J. B. Forrer. 2003. Image segmentation algorithms applied to wood defect detection. Computers and Electronics in Agriculture 41: 157-179. https://doi.org/10.1016/S0168-1699(03)00049-8
  5. Otsu, N. 1979. A thresholding selection method from grey-level histogram. IEEE Transactions on Systems and Man Cybernetics SMC-9: pp. 62-66. https://doi.org/10.1109/TSMC.1979.4310076
  6. Kapur, J. N., P. K. Sahoo, and A. K. Wang. 1985. A new method for grey-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics and Image Processing 29: 273-285. https://doi.org/10.1016/0734-189X(85)90125-2
  7. Zhang, Z. 1998. A Flexible New Technique for Camera Calibration. Technical Report, Microsoft Research, Redmond, WA 98052, USA:21p.
  8. Shi, J. and C. Tomasi. 1994. Good Features to Track. IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94), Seattle, pp. 593-600. https://doi.org/10.1109/CVPR.1994.323794
  9. Lewis, J. P. 1995, Fast Template Matching. Vision Interface, pp. 120-123.
  10. Davies, E. R. 2005. Machine Vision : Theory, Algorithms, Practicalities. Morgan Kaufmann, Elsevier Third Edition, San Francisco, pp. 161-164, pp. 234-245.
  11. 김병남, 이형우. 2001. 화상처리에 의한 목재표면결함 식별에 관한 연구. 목재공학 29(2): 91-99.