Fire night scene restoration method in Mask R-CNN model

A neural network and fire technology, applied in image data processing, editing/combining graphics or text, instruments, etc., can solve the problems of not being able to determine the specific location of the fire, not restoring the night fire scene well, etc., to eliminate competition, Ease of training and reasoning, good detection ability

Inactive Publication Date: 2019-09-13
应急管理部天津消防研究所
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AI Technical Summary

Problems solved by technology

However, due to the limitations of some objective elements, such as the fire occurred at night, the fire investigators could not determine the specific location of the fire only through the surveillance video, and there is still no good way to restore the fire scene at night

Method used

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  • Fire night scene restoration method in Mask R-CNN model
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  • Fire night scene restoration method in Mask R-CNN model

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Embodiment Construction

[0032] like Figure 1 to Figure 4 As shown, the night scene restoration method based on Mask R-CNN neural network uses a computer as a platform, and the steps are as follows:

[0033] ⑴ Establish flame detection sample library:

[0034] In order to use Mask-RCNN to accurately extract and segment the flame area, the collection mainly includes fire monitoring pictures in various scenes at night, and after calibration processing, it is used as a training data set to complete the training of the Mask-RCNN network.

[0035]⑵, image preprocessing:

[0036] Input the video frame of the fire night scene that needs to be restored, perform morphological filtering on each frame image, randomly flip the image, crop, pixel normalize, and image enhancement, which can remove the influence of noise and image size factors, which is convenient for network training and reasoning .

[0037] ⑶, Mask R-CNN model training:

[0038] Mask R-CNN inherits from Faster R-CNN, adds a Mask PredictionBra...

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Abstract

The invention relates to a fire night scene restoration method in a Mask R-CNN model. By training the Mask-RCNN model, flames are segmented and an initial region of flames at a fire position at nightis segmented and integrated into a daytime monitoring video. A specific location of the fire is positioned. In subsequent operation of instance segmentation, an image blending superposition algorithmis used to superimpose the segmented flame region onto the daytime surveillance video image, thereby realizing the restoration of a fire scene and assisting fire location. The method lays a solid foundation for recognition of fire causes.

Description

technical field [0001] The present invention relates to the fields of image processing and computer vision, and in particular to a nighttime fire scene restoration method based on the Mask R-CNN neural network, which integrates the flame at the fire location at night into the daytime monitoring video to realize the restoration of the night fire scene. Background technique [0002] In today's society, fire has always been one of the main disasters faced by human beings. Serious fire accidents will not only cause a large number of casualties, but also endanger lives. Avoiding fires not only requires timely monitoring and early warning of fire accidents before the fire, but also requires accurate investigation and analysis of the cause of the fire and the location of the fire after the fire. Flame is one of the important visual signs of fire, and the study of flame plays an important role in the accurate monitoring of fire. [0003] The traditional fire accident investigation ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/32G06K9/34G06K9/46G06K9/62G06T5/50G06T11/60
CPCG06T5/50G06T11/60G06T2207/20221G06V10/267G06V10/25G06V10/56G06F18/214
Inventor 王鑫陈钦佩鲁志宝
Owner 应急管理部天津消防研究所
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