A Novel Vision-Based Approach for Detection of Foreign Substances

Abstract:

The detection of foreign substances in injection so far is still achieved artificially, which result in low accuracy and low efficiency. This paper focuses on developing a novel vision-based approach for detection of foreign substances. Foreign substances are classified into two categories, subsiding-slowly object and subsiding-fast object. A relative movement caused by a motor helps to distinguished foreign substances from ampoule surface scratches. Moving objects in injection are divided from static ones by a background image derived from two frames. The Mean Shift Embedded Particle Filter (MSEPF) is proposed to detect moving-slowly object while Frame Distance is defined to detect moving-fast object. 200 ampoule samples filled with injection are tested. The integrated detection accuracy with this approach is 98.00%, with 97.56% accuracy for subsiding-slowly objects and 96.67% accuracy for subsiding-fast ones. The result shows that the system can detect foreign substances effectively.

• Periodical: Advanced Materials Research (Volumes 317-319)
• Pages: 847-853
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.317-319.847
• Online since: August 2011
• Authors: Gui Liang Lu, Yu Zhou, Yao Yu, Si Dan Du
• Keywords: Computer Vision, Detection of Foreign Substances, Frame Distance, Object Tracking

References

[1] 10.4 billion bottles of transfusion solution a year. Available from: http://finance.sina.com.cn/g/20101224/10539157952.shtml. In Chinese.

[2] Ishii, A., T. Mizuta and S. Todo in: Fourteenth International Conference on Pattern Recognition, Vols 1 and 2, 1998: pp.1646-1650.

[3] Lu, J., et al. in: 2008 International Conference on Information and Automation, Vols 1-4, 2008: pp.424-429.

[4] Zhou, B. W., et al. in: Paciia: 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Vols 1-3, Proceedings, 2008: pp.1474-1479.

[5] Xiao, F., et al. in: Computer Measurement & Control, Vols 18, 2010: pp.295-298. In Chinese.

[6] Hough, P. V. C. Machine analysis of bubble chamber pictures. 1959: CERN.

[7] Duda, R. O. and P. E. Hart in: Communications of the Acm, Vols 15, 1972: pp.11-15.

[8] Shan, C., T. Tan and Y. Wei in: Pattern recognition, Vols 40, 2007: pp.1958-1970.

[9] Maggio, E. and A. Cavallaro. Hybrid particle filter and mean shift tracker with adaptive transition model. 2005: Citeseer.

DOI: 10.1109/icassp.2005.1415381

[10] Comaniciu, D., V. Ramesh and P. Meer in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003: pp.564-575.

[11] Comaniciu, D., V. Ramesh and P. Meer. Real-time tracking of non-rigid objects using mean shift. 2000: Published by the IEEE Computer Society.

DOI: 10.1109/cvpr.2000.854761