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Flame detection method based on improved ViBe algorithm and lightweight convolutional network

A technology of flame detection and convolutional network, which is applied in the field of flame detection of ViBe algorithm and lightweight convolutional network, can solve the problems of difficult flame area detection tasks, slow detection speed, difficult training, etc., to ensure speed and improve speed , The effect of improving the accuracy of flame detection

Active Publication Date: 2021-03-23
NANJING FORESTRY UNIV
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Problems solved by technology

The existing deep convolutional network model for flame image segmentation has high complexity, difficult training and slow detection speed
Moreover, the public data sets used by these models do not contain flame objects, so it is difficult to apply to real-time flame area detection tasks

Method used

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  • Flame detection method based on improved ViBe algorithm and lightweight convolutional network
  • Flame detection method based on improved ViBe algorithm and lightweight convolutional network
  • Flame detection method based on improved ViBe algorithm and lightweight convolutional network

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Embodiment Construction

[0024] Below in conjunction with accompanying drawing and specific embodiment the case is further described:

[0025] Such as figure 1 As shown, this method of flame area detection based on surveillance video mainly includes two steps, namely the improved ViBe foreground detection algorithm and the lightweight flame detection convolutional neural network based on the Squeeze-and-Excitation (SE) attention mechanism. Network construction. Specifically, it includes the following steps:

[0026] Step 1, utilize the improved ViBe prospect detection algorithm to carry out the prospect detection of flame, concrete steps are:

[0027] Step 1.1 selects the first frame of the video sequence, and utilizes the traditional ViBe algorithm to initialize the background model;

[0028] Step 1.2 defines the flickering degree of pixels in the video frame, as shown in equation (1):

[0029] BL t (x,y)=α×BL t-1 (x,y)+β×S or (x,y) (1)

[0030] Among them, BL t (x, y) indicates the degree o...

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Abstract

The invention discloses a flame detection method based on an improved ViBe algorithm and a lightweight convolutional network. The flame detection method comprises the following steps: carrying out flame foreground detection by utilizing the improved ViBe foreground detection algorithm; and further judging whether the suspected flame area detected by the ViBe foreground detection algorithm is a flame object or not by utilizing the constructed lightweight convolutional neural network, and constructing the lightweight flame detection convolutional neural network based on the SE attention mechanism. According to the invention, a ViBe foreground detection algorithm is used for detecting a suspected flame foreground target, eliminating dynamic background noise points and removing an interferenceobject which is similar to flame in color and does not have flicker characteristics; compared with an existing target detection deep convolutional network, the method has the advantages that the flame detection speed is increased; an SE attention mechanism is embedded into the lightweight flame detection convolutional network, so that the flame detection speed is ensured while the flame detectionprecision is improved.

Description

technical field [0001] The invention belongs to the technical field of video detection, in particular to a flame detection method based on an improved ViBe algorithm and a lightweight convolutional network Background technique [0002] Video fire detection generally consists of two steps: moving foreground detection and flame recognition. However, in reality, there are many disturbances in complex environments, such as background disturbances (shaking leaves, fountains, etc.), light changes, shadow disturbances, etc., which bring challenges to the existing foreground detection algorithms. Once the foreground is detected, a flame recognition step is required. The fire recognition of traditional image processing extracts the geometry, texture, flicker frequency and other features of the flame area and designs a classifier to recognize the flame. Whether the feature selection is reasonable or not directly affects the accuracy of flame recognition. Applying deep learning techn...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/082G06V20/41G06V20/52G06V10/56G06N3/045G06F18/2415
Inventor 赵亚琴赵文轩卢鹏丁志鹏
Owner NANJING FORESTRY UNIV
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