A New Scheme for Refuse Incineration End Point Detection Based on Computer Vision

Article Preview

Abstract:

The paper introduces computer vision into the detection of end points of flame burning during the process of refuse incineration and proposes a method to detect the end points of flame by image processing. Firstly, filter image and enhance the special quality of shade by total variation de-noising. Secondly, extract the main edge of the flame image by method based on binary morphology algorithm. Thirdly, detect and output the end points of flame by median method which has removed the spike of the image. The results show that the presented method is efficient and the system can meet the requirement of both detection accuracy and processing speed. Also it increases the safety of incineration equipment and saves plenty of labor force.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 230-232)

Pages:

769-773

Citation:

Online since:

May 2011

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] The characteristics and applications of flame image detection system [M].

Google Scholar

[2] Qian Guo, The research of ACC mechanical grate incinerator technology of automatic combustion control, Environment protection frontline, 2008. 5, pp.44-45, in Chinese.

Google Scholar

[3] Quan Pang, Yingle Fan, The research of binarization based on edge detection[J] , Measurement technology, 2003(12), p . 1-3, in Chinese.

Google Scholar

[4] Jixiang Sun , Image Analysis [M], Beijing, (2005).

Google Scholar

[5] Kenneth RC. Digital Image Processing [M], Beijing , (1998).

Google Scholar

[6] P. Bao and L. Zhang, Noise reduction for magnetic resonance image via adaptive multiscale products thresholding, IEEE Trans. Medical Imaging, Sept. 2003, vol. (22), pp.1089-1099, in Chinese.

DOI: 10.1109/tmi.2003.816958

Google Scholar

[7] L. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms, Physical D(92) pp.259-268.

DOI: 10.1016/0167-2789(92)90242-f

Google Scholar

[8] Xinbo Gao, Jie Li, Hongbing Ji, A multi-threshold image segmentation algorithm based on weighting fuzzy c-means clustering and statistical test[J], Acta Electronica Sinica, vol. 32(2004), pp.661-665, in Chinese.

Google Scholar