Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects

The manual inspection of Printed Circuit Boards (PCB) is labor intensive and slow down the production line. During the assembly process, the defective PCBs with flux defects if not detected and remove, it can create corrosion and cause harmful effects on the board itself. As such, an automated inspe...

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Main Author:	Ang, Teoh Ong
Format:	Thesis
Language:	English English
Published:	2016
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/18521/1/Computer%20Vision%20Inspection%20And%20Classification%20On%20Printed%20Circuit%20Boards%20For%20Flux%20Defects%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18521/2/Computer%20Vision%20Inspection%20And%20Classification%20On%20Printed%20Circuit%20Boards%20For%20Flux%20Defects.pdf
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id	my-utem-ep.18521
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Ang, Teoh Ong Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects
description	The manual inspection of Printed Circuit Boards (PCB) is labor intensive and slow down the production line. During the assembly process, the defective PCBs with flux defects if not detected and remove, it can create corrosion and cause harmful effects on the board itself. As such, an automated inspection system is very much needed to overcome the aforementioned problems in PCB production line. The main objective of this work is to develop a real-time machine vision system for quality assessment of PCBs by detecting defectives PCBs. The proposed system should be able to detect flux defect on PCB board during the re-flow process and achieve good accuracy of the PCB quality checking. The proposed system is named as An Automatic Inspection System for Printed Circuit Boards (AIS-PCB), involves design and fabrication of a total automation control system involving the use of mechanical PCB loader/un-loader, robotic pneumatic system handler with vacuum cap and a vision inspection station that makes a decision either to accept or reject. The decision making part involves classifier training of PCB images. Prior to ANN training, the images need to be processed by the image processing and feature extraction. The image processing system is based on pattern matching and color image analysis techniques. The shape of the PCB pins is analyzed by using pattern matching technique to detect the PCB flux defect area. After that, the color analysis of the flux defect on a PCB boards are processed based on their red color pixel percentage in Red, Green and Blue (RGB) model. The red color filter band mean value of histogram is measured and compared to the value threshold to determine the occurrence of flux defect on the PCBs. The texture of the PCB flux defect can also be extracted based on line detection of the gradient field PCB images and feature indexing by using Radon transform-based approach. The feed-forward back-propagation (FFBP) model is used as classifier to classify the product quality of the PCBs via a learning concept. A number of trainings using the FFBP are performed for the classifier to learn and match the targets. The learned classifier, when tested on the PCBs from a factory’s production line, achieves a grading accuracy of coefficient of efficiency (COE) greater than 95%. As such, it can be concluded that the developed AIS-PCB system has shown promising results by successfully classifying flux defects in PCBs through visual information and facilitates automatic inspection, thereby aiding humans in conducting rapid inspections.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ang, Teoh Ong
author_facet	Ang, Teoh Ong
author_sort	Ang, Teoh Ong
title	Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects
title_short	Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects
title_full	Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects
title_fullStr	Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects
title_full_unstemmed	Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects
title_sort	computer vision inspection and classification on printed circuit boards for flux defects
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Electrical Engineering
publishDate	2016
url	http://eprints.utem.edu.my/id/eprint/18521/1/Computer%20Vision%20Inspection%20And%20Classification%20On%20Printed%20Circuit%20Boards%20For%20Flux%20Defects%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/18521/2/Computer%20Vision%20Inspection%20And%20Classification%20On%20Printed%20Circuit%20Boards%20For%20Flux%20Defects.pdf
_version_	1747833931723440128
spelling	my-utem-ep.185212021-10-08T13:37:48Z Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects 2016 Ang, Teoh Ong T Technology (General) TK Electrical engineering. Electronics Nuclear engineering The manual inspection of Printed Circuit Boards (PCB) is labor intensive and slow down the production line. During the assembly process, the defective PCBs with flux defects if not detected and remove, it can create corrosion and cause harmful effects on the board itself. As such, an automated inspection system is very much needed to overcome the aforementioned problems in PCB production line. The main objective of this work is to develop a real-time machine vision system for quality assessment of PCBs by detecting defectives PCBs. The proposed system should be able to detect flux defect on PCB board during the re-flow process and achieve good accuracy of the PCB quality checking. The proposed system is named as An Automatic Inspection System for Printed Circuit Boards (AIS-PCB), involves design and fabrication of a total automation control system involving the use of mechanical PCB loader/un-loader, robotic pneumatic system handler with vacuum cap and a vision inspection station that makes a decision either to accept or reject. The decision making part involves classifier training of PCB images. Prior to ANN training, the images need to be processed by the image processing and feature extraction. The image processing system is based on pattern matching and color image analysis techniques. The shape of the PCB pins is analyzed by using pattern matching technique to detect the PCB flux defect area. After that, the color analysis of the flux defect on a PCB boards are processed based on their red color pixel percentage in Red, Green and Blue (RGB) model. The red color filter band mean value of histogram is measured and compared to the value threshold to determine the occurrence of flux defect on the PCBs. The texture of the PCB flux defect can also be extracted based on line detection of the gradient field PCB images and feature indexing by using Radon transform-based approach. The feed-forward back-propagation (FFBP) model is used as classifier to classify the product quality of the PCBs via a learning concept. A number of trainings using the FFBP are performed for the classifier to learn and match the targets. The learned classifier, when tested on the PCBs from a factory’s production line, achieves a grading accuracy of coefficient of efficiency (COE) greater than 95%. As such, it can be concluded that the developed AIS-PCB system has shown promising results by successfully classifying flux defects in PCBs through visual information and facilitates automatic inspection, thereby aiding humans in conducting rapid inspections. UTeM 2016 Thesis http://eprints.utem.edu.my/id/eprint/18521/ http://eprints.utem.edu.my/id/eprint/18521/1/Computer%20Vision%20Inspection%20And%20Classification%20On%20Printed%20Circuit%20Boards%20For%20Flux%20Defects%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/18521/2/Computer%20Vision%20Inspection%20And%20Classification%20On%20Printed%20Circuit%20Boards%20For%20Flux%20Defects.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=100384 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Electrical Engineering 1. Ajay Pal Singh Chauhan, Sharat Chandra Bhardwaj, “Detection of Bare PCB Defects by Image Subtraction Method using Machine Vision”, Proceedings of the World Congress on Engineering, Vol II, July 2011. 2. Akash kasturkar and S.D. Lokhande (2016), PCBs fault detection by image processing tools: A Review. International Journal of Innovative Research in Science, Engineering and Technology. 3. Alvy Ray Smith, “Color Gamut Transform Pairs”, SIGGRAPH 78 conference proceeding, August 1978, 12-19. 4. Anoop K. P, Sarath N.S, Sasi Kumar V. V., "A Review of PCB Defect Detection using Image Processing", International Journal of Engineering and Innovative Technology (IJEIT), Volume 4, Issue 11, May 2015. 5. Aouache, M., H. Aini and A.S. Salina, 2011. A new approach for noise reduction in spine radiograph images using a non-linear contrast adjustment scheme based adaptive factor. Scientific Res. Essays, 6: 4246- 58. 6. AR.Arunachalam, “PCB Defect Detection and Classification by Image Erosion Technique”, International Journal of Innovative Research in Computer and Communication Engineering, Volume 3, Issue 9, September 2015. 7. Ashish Singh, Vimal H. Nayak, “Automatic Detection of PCB Defects”, IJIRST - International Journal for Innovative Research in Science & Technology, Volume 1, Issue 6, November 2014. 8. B. Kaur, G. Kaur, A. Kaur, “Detection and Classification of Printed Circuit Boards Defects”, Scientific online – open transaction on information processing, Volume 1, Number 1, May 2014. 9. Bhardwaj, S.C., 2012. Machine vision algorithm for PCB parameters inspection. Proceedings on National Conference on Future Aspects of Artificial intelligence in Industrial Automation, (FAAIIA’ 12), Foundation of Computer Science, New York, USA., pp: 20-24. 10. Chauhan, A.P.S. and S.C. Bhardwaj, 2011. Detection of bare PCB defects by image subtraction method using machine vision. Proceedings of the World Congress on Engineering, Jul. 6-8, London, UK. 11. Cheol-Hong Moon, H.-C. J.-K., 2009. Implementation of the PCB Pattern Matching System to Detect Defects. FPGA-based PCB Pattern Matching System , 32-38. 12. Chung C., 2014. China Market Outlook for 2015. The PCB Magazine, 26-30. 13. Eduardo Bayro-Corrochano, Review of Automated Visual Inspection 1983 to 1993 – Part I: conventional approaches, SPIE - Intelligent Robots and Computer Vision XII, Vol. 2055, pp. 128-158, 1993. 14. El-Faki, M.S., Zhang, N., Peterson, D.E., 2000a. Factors affecting color-based weed detection. Transactions of the ASAE 43, 1001-1009. 15. El-Faki, M.S., Zhang, N., Peterson, D.E., 2000b. Weed detection using color machine vision. Transactions of the ASAE 43, 1969-1978. 16. Fabiana R. Leta, Flávio F. Feliciano, Flavius P. R. Martins, “Computer Vision System for Printed Circuit Board Inspection”, ABCM Symposium Series in Mechatronics, Volume 3, 2008. 17. Francesca Stern: A Global Perspective, 2015 Outlook for the PCB industry, The PCB magazine, page 12, December 2014. 18. Global Printed Circuit Board market & recyclable PCB market analysis and forecast (2011 - 2018), (2014, January 1). Retrieved from http://www.researchandmarkets.com/research/qzg6f7/global_printed 19. Greenberg, et al., 2006. Method for printed circuit board inspection, patent application publication US 2001/0002935AI. 20. Hamada, T., Nakahata, K., Nomoto, M., Nakagawa, Y., Hashimoto, Y., Karasaki, K., 1990. Automated pattern inspection system for PCB photo masks using design pattern comparison method, Industrial Electronics Society. IECON '90, 16th Annual Conference of IEEE. 21. Hilund C., 2007. The Radon Transform. Aalborg University, Vision, Graphics and Interactive Systems. 22. Ismail Ibrahim, Syed Abdul Rahman Syed Abu Bakar, Musa Mohd Mokji, Kamal Khalil, Zulkifli Md. Yusof, Jameel Abdullah Ahmed Mukred, Mohd Saberi Mohamad, Zuwairie Ibrahim, “An Image Registration Technique to Enhance PCB Inspection Algorithms with Real Images”, ICIC Express Letters, Volume 6, No. 3, March 2012. 23. Ito, M., Nikaido, Y., 1991. Recognition of pattern defects of printed circuit board using topological information, Dept. of Electron. & Int. Eng, Tokyo Univ. of Agriculture. & Technology, Japan; Electronics Manufacturing Technology Symposium, Eleventh IEEE/CHMT International. 24. J. Mandeville, 1985. Novel Method for Analysis of PCB. IBM J. Res. Develop. 29, 73-86. 25. Ja H. Koo and Suk I. Yoo, 1998. A Structural Matching for Two-dimensional Visual Pattern Inspection, Proceedings of IEEE International Conference on Systems, Man, and Cybernetics. 26. Jagadish. S. Jakati, Sidramayya S Matad, 2014. PCB defect detection based on pattern matching and segmentation algorithm. International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 9. 27. Jipsa Kurian and V. Karunakaran, 2012. A Survey on Image Classification Methods, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), Volume 1, Issue 4. 28. Joblove, George H. and Greenberg, Donald, 1978. Color spaces for computer graphics. Computer Graphics 12 (3): 20–25. 29. K.Sundaraj, 2009. PCB Inspection for missing or misaligned components using background subtraction, WSEAS Transactions on Information Science and Applications, Volume 6 Issue 5. 30. Kaur Kamalpreet, Kaur Beant, “PCB Defect Detection and Classification Using Image Processing”, International Journal of Emerging Research in Management &Technology ISSN: 2278-9359, Volume 3, Issue 8, August 2014. 31. Lamm, R.D., Slaughter, D.C., Giles, D.K., 2002. Precision weed control for cotton. Transactions of the ASAE 45, 231–238. 32. Le Hoang Thai, Tran Son Hai and Nguyen Thanh Thuy, 2012. Image classification using Artificial Neural Network (ANN) and Support Vector Machine (SVM), I.J. Information Technology and Computer Science, 2012, 5, 32-38, Published Online May 2012 in MECS. 33. Leta, F. R. et al., 2005. Discussing Accuracy in an Automatic Measurement System using Computer Vision Techniques, COBEM 2005. 34. Malge P. S., Nadaf R. S., 2014. A Survey: Automated Visual PCB Inspection Algorithm, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 3 Issue 1. 35. Malge P.S., Nadaf R.S., “PCB Defect Detection, Classification and Localization using Mathematical Morphology and Image Processing Tools”, International Journal of Computer Applications (0975 – 8887), Volume 87 – No.9, February 2014. 36. Mao, W., Wang, Y., Wang, Y., 2003. Real-time detection of between-row weeds using machine vision. ASAE paper number 031004. The Society for Agricultural, Food, and Biological Systems, St. Joseph, MI. 37. Mar, N. S., 2011. Design and Development of Automatic Visual Inspection System for PCB Manufacturing. PCB Manufacturing , 29. Volume 27, Issue 5, October 2011, Pages 949–962, ELSEVIER. 38. Mar, N.S.S., C. Fookes and P.K. Yarlagadda, 2009. Design of automatic vision-based inspection system for solder joint segmentation. J. Ach. Mater. Manufact. Eng., 34: 145-151. 39. Marchant, J.A., Andersen, H.J., Onyango, C.M., 2001. Evaluation using different waveband combinations. Computers and Electronics in Agriculture 32, 101–117 (Elsevier). 40. Markets and Markets, 2014. Machine Vision Market Worth $9.50 Billion by 2020. [Online] Available at: http://www.marketsandmarkets.com/PressReleases/machine-vision-systems.asp [Accessed on 16 April 2015] 41. Mashohor, S., J.R. Evans and T. Arslan, 2004. Genetic algorithm based Printed Circuit Board (PCB) inspection system. Proceedings of the IEEE International Symposium Consumer Electronics, Sept. 1-3, IEEE Xplore Press, pp: 519-522. DOI: 10.1109/ISCE.2004.1376000. 42. Meyer, G.E., Mehta, T., Kocher, M.F., Mortensen, D.A, Samal, A., 1998. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Transactions of the ASAE 41(4): 1189-1197. 43. Moganti, F. Ercal, C. Dagli, S. Tsunekawa, 1996. Automatic PCB Inspection Algorithms: A Survey, Computer Vision and Image Understanding 63, 287-313. 44. Mohit Borthakur, Anagha Latne, Pooja Kulkarni, "A Comparative Study of Automated PCB Defect Detection Algorithms and to Propose an Optimal Approach to Improve the Technique". International Journal of Computer Applications (0975 – 8887), Volume 114 – No.6, March 2015. 45. N. Otsu, 1979. A threshold selection method from gray-level histogram, IEEE Transactions on System Man Cybernetics, Vol. SMC-9, No. 1, pp. 62-66. 46. Namita Kalyan Shinde, S.S.Morade, "PCB Inspection System using Image Processing", International Journal of Science, Engineering and Technology Research (IJSETR), Volume 4, Issue 4, April 2015. 47. Neelum Dave, Vikas Tambade and Balaji Pandhare (2016), PCB defect detection using image processing and embedded system. International Research Journal of Engineering and Technology (IRJET), Volume: 03 Issue: 05 \| May-2016. 48. Noor Khafifah Khalid, Zuwairie Ibrahim, Mohamad Shukri Zainal Abidin, “An Algorithm to Group Defects on Printed Circuit Board for Automated Visual Inspection”, Faculty of Electrical Engineering, Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Vol. 9, No. 2, May 2008. 49. Noridayu Manshor and Dhanesh Ramachandram, 2012. Feature Fusion in Improving Object Class Recognition, Journal Computer Science. 50. P. K. Sahoo, S. Soltani, And A. K. C. Wong, “A Survey of Thresholding Techniques”, Academic Press, Inc., Computer Vision, Graphics, And Image Processing 41, 233-260 (1988) 51. P.Sundara Kumar, T.V.Praveen, M. Anjanaya Prasad,” Artificial Neural Network Model for Rainfall-Runoff -A Case Study”, International Journal of Hybrid Information Technology, Volume 9, No. 3, 2016, pp. 263-272. 52. Phillip Lipson, Geert van der Zalm, Bosch Rexroth Corp., 05/11/2011. Inside Machines: PC versus PLC: Comparing control options, To choose between a PLC or PC, analyze and compare characteristics that could differentiate the two technologies, such as operation, robustness, serviceability, hardware integration, security, safety, programming, and cost. 53. Pooja Kamavisdar, Sonam Saluja and Sonu Agrawal, 2013. A survey on image classification approaches and techniques, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 2, Issue 1. 54. Quality Magazine, 2015. North American Machine Vision Market Increased 12 Percent in 2014. [Online] Available at: http://www.qualitymag.com/articles/92382-north-american-machine-vision-market-increased-12-percent-in-2014 [Accessed on 16 April 2015] 55. Rasika R.Chavan, Swati A.Chavan et al. (2016), Quality Control of PCB using Image Processing. International Journal of Computer Applications (0975 – 8887), Volume 141 – No.5, May 2016. 56. Reference for tin / lead and lead-free solder assembly, AIM, 2008. 57. Roberto L. S. Monteiro, Tereza Kelly G. Carneiro, José Roberto A. Fontoura, Valéria L. da Silva, Marcelo A. Moret, Hernane Borgesde Barros Pereira, “A Model for Improving the Learning Curves of Artificial Neural Networks”, PLoS ONE – Public Library of Science, February 22 – 2016. 58. S. Daniel Madan Raja, Dr. A. Shanmugam, 2011. ANN and SVM Based War Scene Classification using Wavelet Features: A Comparative Study, Journal of Computational Information Systems, pp. 1402-1411–86. 59. S. Kanimozhi, K. Gopala Krishnan, “A Review on Automatic Bare PCB Board Testing”, international journal for science & technology, volume 4, issue 1, July 2014. 60. S. Kaushik and J. Ashraf, 2012. Automatic PCB defect detection using image subtraction method. International Journal of Computer Science and Network (IJCSN). 61. Saman Mohammadi, Maaroof Siosemarde, “Application of Artificial Neural Networks in Order to Predict Mahabad River Discharge”, Open Journal of Ecology, June 2016, 427-434 http://dx.doi.org/10.4236/oje.2016.67040 62. Sanaullah Ahmad Rizvi, K.B.Neelima, Dr. T. Saravanan, "Image Processing Based Defect Detection of Printed Circuit Board", International Journal for Research in Applied Science and Engineering (IJRASET), Volume 3, Issue IV, ISSN: 2321-9653 April 2015. 63. Sanz, J. L. C. and Jain, A.K., 1986. Machine-Vision Techniques for Inspection of Printed Wiring Boards and Thick-Film Circuits, Journal of Optical Society of America A, Vol. 3, No. 9, pp. 1465-1482. 64. Saravanan K and S. Sasithra, 2014. Review on classification based on artificial neural networks (ANN), International Journal of Ambient Systems and Applications (IJASA) Vol.2, No.4. 65. Shashikumar Vishwakarma, SahilTikke, Chinmay Manurkar, Ankit Thanekar, “PCB Detection and Classification Using Digital Image processing”, International Journal of Advance Research in Science and Engineering, Vol. No.4, Special Issue 01, March 2015. 66. Singh, S. and M. Bharti, 2012. Image processing based automatic visual inspection system for PCBs. ISOR J. Eng., 2: 1451-1455. 67. Smith, Tennyson, Photoelectron Emission from Aluminum and Nickel Measured in Air, Journal of Applied Physics, 46(4), p. 1533, 1975 68. Smith, Tennyson, Quantitative Techniques for Monitoring Surface Contamination, in Mittal, ed., Surface Contamination – Genesis, Detection and Control, Plenum Press, New York, pp 697 – 712, 1979. 69. Snehal Rane, Vartika Rai, Sukeshna Awate, "PCB Fault Detection using Embedded Image Processing", International Journal of Advanced Research in Computer and Communication Engineering, Volume 5, Issue 3, March 2016. 70. Sonal D Kalro, Meghashree B S, Prathiksha B G, Suhasini A, H D Phaneendra, “Using Image Processing for Detecting Defects in Printed Circuit Board”, International Journal of Computer Techniques, Volume 2, Issue 2, Mar - Apr 2015. 71. Sonal Kaushik, Javed Ashraf, “Automatic PCB Defect Detection Using Image Subtraction Method”, International Journal of Computer Science and Network (IJCSN), Volume 1, Issue 5, October 2012. 72. Soo Beom Park et al., 2004. Method of content-based image classification using a neural network, ELSEVIER Volume 25, Issue 3, Page 287-300. 73. Suhasini A, Sonal D Kalro , Prathiksha B G, Meghashree BS, Phaneendra H D, "PCB Defect Detection Using Image Subtraction Algorithm", International Journal of Computer Science Trends and Technology (IJCST) - Volume 3 Issue 3, ISSN: 2347-8578 www.ijcstjournal.org, May-June 2015. 74. Surendra Khushwaha, Dinesh Goyal, Rahul Kumar, “Computer Vision System for Automatic PCB Inspection & Quality Analysis with Auto Rejection”, International Journal of Digital Application & Contemporary Research, Volume 4, Issue 2, September 2015. 75. Swapnali Raul, Sneha Sontate, Kshitija Takbide, S.Shelke, "PCB Defect Recognition and Categorization using Image Processing Techniques", International Journal for Scientific Research & Development (IJSRD), Volume 4, Issue 01, 2016. 76. Tsai, Du-Ming and Tsai, Ya-Hui, 2002. Rotation-invariant pattern matching with color ring-projection. Department of industrial engineering and Management, Yuan-Ze University, Chung–Li, Taiwan. 77. Vikas Krishnaji Salunkhe, Babasaheb Gopal Patil, Suryakant R Dodmise, Neural Ravsaheb Patel, “A Review on Study of Fault Detection System for Assembled PCB”, International Journal of Engineering Trends and Technology (IJETT), Volume 34, No. 3, April 2016. 78. Vinita, Shivkant kaushik, JEC Jaipur, "PCB Fault Detection by Image Subtraction Method", International Journal of Scientific Engineering and Applied Science (IJSEAS), Volume 2, Issue 1, ISSN: 2395-3470 January 2016. 79. W. Y. Wu, M. J. Wang and C. M. Liu, 1996. Automated inspection of printed circuit boards through machine vision, Computers in Industry, vol.28, no.2, pp. 103-111. 80. Wang, N., Zhang, N., Dowell, F.E., Sun, Y., Peterson, D.E., 2001. Design of an optical weed sensor using plant spectral characteristics. Transactions of the ASAE 44, 409-419. 81. Woebbecke, D.M., G.E. Meyer, K. Von Bargen, & D.A. Mortensen, 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 38(1): 259-269. 82. Yang, C.C., Prasher, S.O., Landry, J., Ramaswamy, H.S., 2003. Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications. Precision Agriculture 4, 5–18. 83. Yu-guo Wang and Hua-peng Li, 2010. Remote sensing image classification based on artificial neural network, International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), Vol.1, Issue.2. 84. Zeng, Z., L. Ma and M. Suwa, 2011. Algorithm of locating PCB components based on colour distribution of solder joints. Int. J. Adv. Manufact. Technol., 53: 601-614. DOI: 10.1007/s00170-010-2850-9.

Computer Vision Inspection And Classification On Printed Circuit Boards For Flux Defects

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