This research proposes a method to detect and classify the smoke particles of common household fires by analysing the image histogram features of smoke particles generated by Rayleigh scattered light. This research was motivated by the failure of commercially available photoelectric smoke detectors to detect smoke particles less than 100 nm in diameter, such as those in polyurethane (in furniture) fires, and the occurrence of false positives such as those caused by steam.
Seven different types of particles (pinewood smoke, polyurethane smoke, steam, kerosene smoke, cotton wool smoke, cooking oil smoke and a test Smoke) were selected and exposed to a continuous spectrum of light in a closed particle chamber. A significant improvement over the common photoelectric smoke detectors was demonstrated by successfully detecting and classifying all test particles using colour histograms. As Rayleigh theory suggested, comparing the intensities of scattered light of different wavelengths is the best method to classify different sized particles. Existing histogram comparison methods based on histogram bin values failed to evaluate a relationship between the scattered intensities of individual red, green and blue laser beams with different sized particles due to the uneven particles movements inside the chamber.
The current study proposes a new method to classify these nano-scale particles using the particle density independent intensity histograms feature; Maximum Value Index. When a Rayleigh scatter (particles that have the diameter which is less than one tenth of the incident wavelength) is exposed to a light with different wavelengths, the intensities of scattered light of each wavelength is unique according to the particle size and hence, a single unique maximum value index in the image intensity histogram can be detected.
Each captured image in the video frame sequence was divided into its red, green and blue planes (single R, G, B channel arrays) and the particles were isolated using a modified frame difference method. Mean and the standard deviation of the Maximum Value Index of intensity histograms over predefined number of frames (N) were used to differentiate different types of particles. The proposed classification algorithm successfully classified all the monotype particles with 100% accuracy when N ≥ 100. As expected, the classifier failed to distinguish wood smoke from other monotype particles due to the rapid variation of the maximum value index of the intensity histograms of the consecutive images of the image sequence since wood smoke is itself a complex composition of many monotype particles such as water vapour and resin smoke. The results suggest that the proposed algorithm may enable a smoke detector to be safer by detecting a wider range of fires and reduce false alarms such as those caused by steam.
| Identifer | oai:union.ndltd.org:canterbury.ac.nz/oai:ir.canterbury.ac.nz:10092/10866 |
| Date | January 2015 |
| Creators | Pahalawatta, Kapila Kithsiri |
| Publisher | University of Canterbury. Computer Science and Software Engineering |
| Source Sets | University of Canterbury |
| Language | English |
| Detected Language | English |
| Type | Electronic thesis or dissertation, Text |
| Rights | Copyright Kapila Kithsiri Pahalawatta, http://library.canterbury.ac.nz/thesis/etheses_copyright.shtml |
| Relation | NZCU |
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