A fire early warning method based on machine learning to monitor smoke in video images
A machine learning and video image technology, applied in neural learning methods, instruments, computer components, etc., can solve the problem that the classifier cannot accurately distinguish smoke, etc., and achieve the effect of improving the fire warning rate and reducing false alarms
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[0055] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.
[0056] Such as figure 1 As shown, the method specifically includes the following steps:
[0057] Step 1) Collect and mark image data sets of various smoke scenes, among which non-fire warning smoke scenes are classified into category A, and fire warning smoke scenes are classified into category B.
[0058] Non-fire warning smoke scenes are classified into category A, including: setting off firecrackers, car exhaust emissions, existing firefighters extinguishing fires, temples burning incense and smoking, picnic fires and smoke, chimney smoke and other outdoor smoke scenes ; The fire warning smoke scene is classified into category B, including: building fire scene, forest fire scene, warehouse ...
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