CN112132092A - Fire extinguisher and fire blanket identification method based on convolutional neural network - Google Patents
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Abstract
The invention discloses a method for identifying a fire extinguisher and a fire blanket based on a convolutional neural network.A monitoring system acquires a monitoring video of an oil discharge area of a gas station in real time, and intercepts an image every preset time to obtain a real-time image set; dividing a fire extinguisher position area and a fire blanket position area in the real-time image set to obtain a marked image set; defining a fire extinguisher position area and a fire blanket position area in a video, wherein 0 represents that no fire extinguisher exists, 1 represents that no fire blanket exists, 2 represents other abnormal conditions, and 3 represents that the fire extinguisher and the fire blanket exist; constructing a convolutional neural network, and training the marked image set by using the convolutional neural network to obtain a trained convolutional neural network; and sequentially judging the marked image set according to the fire extinguisher area and the fire blanket area by using the trained convolutional neural network, if the output is 3, judging that the states of the fire extinguisher and the fire blanket are normal, and judging that abnormal conditions exist under other conditions.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a method for identifying a fire extinguisher and a fire blanket based on a convolutional neural network.
Background
And (3) installing a camera in the oil unloading area at the beginning of the establishment of the gas station according to the security protection requirement, and inspecting the oil unloading safety operation, the oil unloading quality detection operation and the safety operation of the oil unloading area in a camera monitoring mode.
The fire extinguisher and the fire blanket are placed at specific positions in the oil unloading process, which is an important link for safety preparation in the oil product process, and in the actual oil unloading operation, the situation that the fire extinguisher or the fire blanket is not placed, and the fire extinguisher or the fire blanket is not placed at the specific positions frequently occurs, so that great potential safety hazards are brought to the oil unloading operation. In order to avoid the potential safety hazard caused by the situation, a specially-assigned person is arranged to supervise each oil unloading process through monitoring in the conventional treatment mode. When the potential safety hazard occurs, the situation cannot be transmitted to oil enterprise management personnel at the first time; on the other hand, the management of the personnel at the station of the gas station cannot be implemented efficiently.
In the prior art, a method of human intervention is completely adopted, a camera is used for monitoring whether a fire extinguisher and a fire blanket are placed at a specific position in the oil unloading process, and an objective, non-manual and accurate safety preparation in the intelligent monitoring oil unloading process is not provided. This method, which relies on manual completion, has three problems:
1. the human cost is high, needs the staff to carry out real time monitoring.
2. The risk of error is high and manual inspection always leads to errors due to occasional fatigue or inadvertence.
3. The superior leader can not supervise and manage the method basically.
Disclosure of Invention
In order to solve the problem that the gestures of the oiler are supervised only through manual intervention in the prior art, the invention provides a method for identifying a fire extinguisher and a fire blanket based on a convolutional neural network.
The invention is realized by the following technical scheme:
a fire extinguisher and fire blanket identification method based on a convolutional neural network comprises the following steps:
s1: the monitoring system collects monitoring videos of the oil discharge area of the gas station in real time, and captures an image every preset time to obtain a real-time image set;
s2: dividing a fire extinguisher position area and a fire blanket position area in the real-time image set to obtain a marked image set;
s3: defining a fire extinguisher position area and a fire blanket position area in a video, wherein 0 represents that no fire extinguisher exists, 1 represents that no fire blanket exists, 2 represents other abnormal conditions, and 3 represents that the fire extinguisher and the fire blanket exist;
s4: constructing a convolutional neural network, and training the marked image set by using the convolutional neural network to obtain a trained convolutional neural network;
s5: sequentially judging the marked image set according to fire extinguisher area first and fire blanket area partition by using the trained convolutional neural network, and judging that no fire extinguisher exists if the output is 0; if the output is 1, judging that no fire blanket exists; if the output is 2, judging that other abnormal conditions exist; if the output is 3, the states of the fire extinguisher and the fire blanket are judged to be normal.
On the basis of the scheme, the method further comprises the following steps: the monitoring system in the step S1 comprises a plurality of cameras, the horizontal distance between the installation position of each camera and an oil discharge interface in an oil discharge area monitored by the corresponding camera is 7-8 m, and the height from the ground is 6-8 m.
On the basis of the scheme, the method further comprises the following steps: the step S4 includes the following sub-steps:
s41: selecting a training dataset and a validation dataset;
s42: defining standard convolution kernels with a convolution kernel size of 3 x n and a parameter number of 3 x n;
s43: building a convolutional neural network, inputting 128 × 3 from the input end of the convolutional neural network, and outputting 1 × 4 from the output end of the convolutional neural network through 8 times of convolution operation and 4 times of pooling operation, wherein the instantaneous output data are probabilities of four types of data, namely 0, 1, 2 and 3;
s44: defining a Loss function Loss, wherein the calculation formula of the Loss function Loss is as follows:
wherein y is,
Outputting a category and an actual data label for the network, wherein a, b and c are network hyper-parameters;
s45: training the training set by using a gradient descent method through a loss function to optimize a convolutional neural network;
s46: and (3) verifying the verification set by using the convolutional neural network, and ending the training of the convolutional neural network when the verification precision is more than 95% and is not improved any more, thereby obtaining the trained convolutional neural network.
On the basis of the scheme, the method further comprises the following steps: the training data set in step S41 includes 30000 tagged images, and the verification data set includes 3000 tagged images.
On the basis of the scheme, the method further comprises the following steps: the training data set and the verification data set in step S41 each include four types of data, i.e., 0, 1, 2, and 3, in a ratio of 1:1:1: 3.
On the basis of the scheme, the method further comprises the following steps: the values of the network hyper-parameters a, b and c in the step S44 are respectively: a is 1, b is 5 and c is 0.4.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention upgrades and enables the original monitoring system of the gas station by combining the mode of collecting the image of the refueling area by the camera and the intelligent analysis algorithm, and replaces the original staff to carry out real-time monitoring. The method can intelligently monitor the safety preparation link in the oil unloading process of the gas station, can ensure timely, objective and accurate analysis due to machine operation, and can enable a superior leader to clearly and clearly know the state of the safety preparation link of each oil unloading through the algorithm analysis result, thereby facilitating supervision and management.
Drawings
A further understanding of the embodiments of the present invention may be obtained from the following claims of the invention and the following description of the preferred embodiments when taken in conjunction with the accompanying drawings. Individual features of the different embodiments shown in the figures may be combined in any desired manner in this case without going beyond the scope of the invention. In the drawings:
FIG. 1 is a logic flow diagram of the present invention;
FIG. 2 is a schematic diagram of an embodiment of a high-speed camera;
FIG. 3 is a convolution kernel of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example (b):
as shown in fig. 1, in this embodiment, a method for identifying a fire extinguisher and a fire blanket based on a convolutional neural network includes the following steps:
s1: the monitoring system collects monitoring videos of the oil discharge area of the gas station in real time, and captures an image every preset time to obtain a real-time image set;
s2: dividing a fire extinguisher position area and a fire blanket position area in the real-time image set to obtain a marked image set;
s3: defining a fire extinguisher position area and a fire blanket position area in a video, wherein 0 represents that no fire extinguisher exists, 1 represents that no fire blanket exists, 2 represents other abnormal conditions, and 3 represents that the fire extinguisher and the fire blanket exist;
s4: constructing a convolutional neural network, and training the marked image set by using the convolutional neural network to obtain a trained convolutional neural network;
s5: sequentially judging the marked image set according to fire extinguisher area first and fire blanket area partition by using the trained convolutional neural network, and judging that no fire extinguisher exists if the output is 0; if the output is 1, judging that no fire blanket exists; if the output is 2, judging that other abnormal conditions exist; if the output is 3, the states of the fire extinguisher and the fire blanket are judged to be normal.
As shown in fig. 2, the monitoring system in step S1 includes a plurality of cameras, and the cameras are installed at a horizontal distance of 7.6 meters from the oil discharge interfaces in the oil discharge areas monitored by the corresponding cameras, and have a height of 7 meters from the ground.
Preferably, the step S4 includes the following sub-steps:
s41: selecting a training dataset and a validation dataset;
s42: defining a standard convolution kernel, as shown in fig. 3, with a convolution kernel size of 3 × n and a parameter number of 3 × n;
s43: building a convolutional neural network, inputting 128 × 3 from the input end of the convolutional neural network, and outputting 1 × 4 from the output end of the convolutional neural network through 8 times of convolution operation and 4 times of pooling operation, wherein the instantaneous output data are probabilities of four types of data, namely 0, 1, 2 and 3;
s44: in the method, the accuracy of the class 3 is particularly concerned, and the influence of other three classes is not concerned, so that the method designs and self-defines a weighted Loss function, a network learns the class concerned by the network, a Loss function Loss is defined, and a calculation formula of the Loss function Loss is as follows:
wherein y is,
Outputting a category and an actual data label for the network, wherein a, b and c are network hyper-parameters;
s45: training the training set by using a gradient descent method through a loss function to optimize a convolutional neural network;
s46: and (3) verifying the verification set by using the convolutional neural network, and ending the training of the convolutional neural network when the verification precision is more than 95% and is not improved any more, thereby obtaining the trained convolutional neural network.
On the basis of the scheme, the method further comprises the following steps: the training data set in step S41 includes 30000 tagged images, and the verification data set includes 3000 tagged images.
On the basis of the scheme, the method further comprises the following steps: the training data set and the verification data set in step S41 each include four types of data, i.e., 0, 1, 2, and 3, in a ratio of 1:1:1: 3.
On the basis of the scheme, the method further comprises the following steps: the values of the network hyper-parameters a, b and c in the step S44 are respectively: a is 1, b is 5 and c is 0.4.
The invention can be seen by combining the embodiment, the original monitoring system of the gas station is upgraded and energized by combining the mode of acquiring the image of the refueling area by the camera and the intelligent analysis algorithm, and the original staff is replaced for real-time monitoring. The method can intelligently monitor the safety preparation link in the oil unloading process of the gas station, can ensure timely, objective and accurate analysis due to machine operation, and can enable a superior leader to clearly and clearly know the state of the safety preparation link of each oil unloading through the algorithm analysis result, thereby facilitating supervision and management.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes that are changed from the content of the present specification and the drawings, or are directly or indirectly applied to other related technical fields are included in the scope of the present invention.
Claims (6)
1. A fire extinguisher and fire blanket identification method based on a convolutional neural network is characterized by comprising the following steps:
s1: the monitoring system collects monitoring videos of the oil discharge area of the gas station in real time, and captures an image every preset time to obtain a real-time image set;
s2: dividing a fire extinguisher position area and a fire blanket position area in the real-time image set to obtain a marked image set;
s3: defining a fire extinguisher position area and a fire blanket position area in a video, wherein 0 represents that no fire extinguisher exists, 1 represents that no fire blanket exists, 2 represents other abnormal conditions, and 3 represents that the fire extinguisher and the fire blanket exist;
s4: constructing a convolutional neural network, and training the marked image set by using the convolutional neural network to obtain a trained convolutional neural network;
s5: sequentially judging the marked image set according to fire extinguisher area first and fire blanket area partition by using the trained convolutional neural network, and judging that no fire extinguisher exists if the output is 0; if the output is 1, judging that no fire blanket exists; if the output is 2, judging that other abnormal conditions exist; if the output is 3, the states of the fire extinguisher and the fire blanket are judged to be normal.
2. The convolutional neural network based fire extinguisher and fire blanket identification method as claimed in claim 1, wherein the monitoring system in step S1 comprises a plurality of cameras, the cameras are installed at a horizontal distance of 7-8 m from the oil discharge interface in the oil discharge area monitored by the corresponding cameras, and the height from the ground is 6-8 m.
3. The convolutional neural network based fire extinguisher and fire blanket identification method as claimed in claim 1, wherein said step S4 comprises the following sub-steps:
s41: selecting a training dataset and a validation dataset;
s42: defining standard convolution kernels with a convolution kernel size of 3 x n and a parameter number of 3 x n;
s43: building a convolutional neural network, inputting 128 × 3 from the input end of the convolutional neural network, and outputting 1 × 4 from the output end of the convolutional neural network through 8 times of convolution operation and 4 times of pooling operation, wherein the instantaneous output data are probabilities of four types of data, namely 0, 1, 2 and 3;
s44: defining a Loss function Loss, wherein the calculation formula of the Loss function Loss is as follows:
wherein y is,
Outputting a category and an actual data label for the network, wherein a, b and c are network hyper-parameters;
s45: training the training set by using a gradient descent method through a loss function to optimize a convolutional neural network;
s46: and (3) verifying the verification set by using the convolutional neural network, and ending the training of the convolutional neural network when the verification precision is more than 95% and is not improved any more, thereby obtaining the trained convolutional neural network.
4. The convolutional neural network based fire extinguisher and fire blanket identification method as claimed in claim 3, wherein the training data set in step S41 comprises 30000 tagged images and the verification data set comprises 3000 tagged images.
5. The convolutional neural network based fire extinguisher and fire blanket identification method as claimed in claim 3, wherein the training data set and the verification data set in step S41 each contain four types of data of 0, 1, 2 and 3 in a ratio of 1:1:1: 3.
6. The method for identifying fire extinguishers and fire blankets based on convolutional neural network as claimed in claim 3, wherein the values of the network hyper-parameters a, b and c in the step S44 are respectively: a is 1, b is 5 and c is 0.4.
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