Unlock instant, AI-driven research and patent intelligence for your innovation.

Fire early warning method and system based on semantic segmentation and recognition

A technology of fire early warning and semantic segmentation, which is applied to fire alarms that rely on smoke/gas effects, character and pattern recognition, neural learning methods, etc., can solve the problems of poor detection of small target fire areas and achieve good segmentation effects The effect of enhancing accuracy

Pending Publication Date: 2021-12-07
合肥中科类脑智能技术有限公司
View PDF3 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: how to solve the problem of poor detection of small target fire areas, and provide a fire early warning method based on semantic segmentation and recognition

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Fire early warning method and system based on semantic segmentation and recognition
  • Fire early warning method and system based on semantic segmentation and recognition
  • Fire early warning method and system based on semantic segmentation and recognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0036] Such as figure 1 As shown, the present embodiment provides a technical solution: a fire early warning method based on semantic segmentation and recognition, comprising the following steps:

[0037] Step 1: First send the image to be tested to the segmentation network to obtain the segmentation result of the fire area;

[0038] Step 2: Combine the segmentation results output by the segmentation network with the image to be tested to obtain images of the fire and the area around the fire;

[0039] The output of the segmentation network is a probability map. After binarization, a binary map is obtained, and the connected area analysis is performed on the image. Each connected area is a suspected fire area, and the connected area with a smaller area is filtered out. , find the minimum circumscribed horizontal rectangle for the remaining connected areas, and then map this rectangle to the image to be tested to obtain the suspected fire area image; in short, this step obtain...

Embodiment 2

[0050] In this embodiment, the image to be tested is first input into the segmentation network to obtain the segmentation result of the fire area; as Figure 4 shown; then according to the segmentation results, combined with the original image to be tested, the fire and its surrounding areas in the image are obtained. In practical applications, according to the size of the fire area, it is enlarged by 1.3 times as the final fire area. Such as Figure 5 shown. Input the pictures of the fire and its surrounding area into the recognition network for identification, if it is a fire, it will give an early warning, if it is not a fire, it will not give an early warning. non-fire areas such as Image 6 shown.

[0051] In summary, the fire early warning method based on semantic segmentation and recognition in the above embodiment uses a deep learning network to segment the fire area, which is better than the traditional manual method; it makes full use of the prior knowledge that s...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a fire early warning method and system based on semantic segmentation and recognition, and belongs to the technical field of fire early warning; the method comprises the following steps: sending a to-be-detected image into a segmentation network to acquire a segmentation result of a fire region; combining a segmentation result output by the segmentation network with the to-be-detected image to obtain a fire and a fire surrounding area image; sending the images of the fire and the surrounding area of the fire into a discrimination network, and discriminating the fire; if the fire hazard exists, giving out early warning, or otherwise, not executing the early warning. According to the invention, the deep learning network is adopted for fire region segmentation, and the segmentation effect is better than that of a traditional manual method; secondly, the prior knowledge that smoke always exists around the fire disaster is fully utilized for fire disaster recognition, and the judgment accuracy is enhanced; finally, compared with a common identification network, the fine-grained identification network can better distinguish fire and non-fire areas.

Description

technical field [0001] The invention relates to the technical field of fire early warning, in particular to a fire early warning method and system based on semantic segmentation and recognition. Background technique [0002] Most of the existing fire detection schemes adopt the target detection scheme based on deep learning. Although the deep learning method has greatly improved the effect of target detection, it still has the problem of poor detection effect on small target objects. At present, the methods to solve this kind of problems are: 1. Fusion area segmentation fire detection, using traditional machine learning methods to extract the color information and brightness information of the fire area to segment the fire area, so as to obtain better detection results; 2. Combined with the detection method of carbon dioxide concentration, the phenomenon that the concentration of carbon dioxide in the air increases after a fire occurs, improves the accuracy of early warning ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/00G06K9/32G06K9/34G06K9/62G06N3/04G06N3/08G06Q50/26G08B17/10
CPCG06N3/08G06Q50/265G08B17/10G06N3/045G06F18/24
Inventor 康凯刘海峰任广鑫张明
Owner 合肥中科类脑智能技术有限公司