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Tip

Camera Video stream and scene requirements for the Fire Detectiondetector

Hardware requirements for neural analytics operation

Optimizing the operation of neural analytics on GPU in Windows OS

To configure smoke (fire) detection tool:

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Optimizing the operation of neural analytics on GPU in Linux OS

To configure the Fire detector, do the following:

  1. Go to the Detection Tools tab.
  2. Below the required camera, click Create…  Category: Production Safety → Fire detector.

By default, the detector is enabled and set to detect fire.

If necessary, you can change the detector parameters. The list of parameters is given in the table:

ParameterValueDescription
Object features
Record mask to archiveYesBy default, the sensitivity scale of the

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detector is recorded to the archive (see Displaying information from a

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detector (mask)). To disable the parameter, select

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the No value
No
Video streamMain streamIf the camera supports multistreaming, select the stream for which detection is needed

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. Selecting a low

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quality video stream

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reduces the load on the Server
Other
EnableYesThe detector is enabled by default. To disable the detector, select the No value
No
NameFire detector
Enter the detector name or leave the default name
Decoder modeAutoSelect a processing resource for decoding video streams

...

. When you select a GPU, a stand-alone graphics card takes priority (when decoding with NVIDIA NVDEC chips). If there is no appropriate GPU, the decoding will use the Intel Quick Sync Video technology. Otherwise, CPU resources will be used for decoding
CPU
GPU
HuaweiNPU
Number of frames processed per second0.1

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Specify the number of frames that the detector will process per second

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. The value

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must be in

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the range [0

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.016; 100]

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...

Type

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titleNote

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The default values (five frames for output and 0,1 FPS) indicate that the tool will analyze frame over 50 seconds span. The detection tool analyzes one frame every 10 seconds. If it detects smoke/fire on five consecutive fames, the detection tool will trigger an alert.

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Note
titleAttention!
  • It may take several minutes to launch the algorithm on NVIDIA GPU after you apply the settings. You can use caching to speed up future launches (see Optimizing the operation of neural analytics on GPU).
  • If you specify other processing resource than the CPU, this device will carry the most of computing load. However, the CPU will also be used to run the detection tool.
  • Starting with Detector Pack 3.11, Intel HDDL and Intel NCS aren’t supported.

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Fire detector
Name of the detector type (non-editable field)
Advanced settings
Neural network file

Select a neural network file. The standard neural networks for different processor types are located in the C:\Program Files\Common Files\AxxonSoft\DetectorPack\NeuroSDK directory. You don't need to select the standard neural networks in this field, the system will automatically select the required one. If you use a custom neural network, enter a path to the file.

Info
titleNote

For the correct neural network operation on Linux OS, place the corresponding file in the /opt/AxxonSoft/DetectorPack/NeuroSDK directory. 

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Note
titleAttention!

If you use standard neural network (training wasn't performed in operating conditions), we guarantee the overall accuracy of 80-95% and the percentage of false positives of 5-20%.

Ignore black and white imageYesThe parameter is disabled by default. If you don't want the detector to generate an event when the image is black and white, select the Yes value
No
Number of measurements in a row to trigger detection5

Specify the minimum number of frames with

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fire

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for

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the detector to generate an event. The value

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must be in the range [5; 20]

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Scanning modeYesThe parameter is disabled by default. You can use the scanning mode (see Configuring the scanning mode) to detect small objects or objects in areas far away from the camera

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. For this, select the Yes value. This mode doesn’t provide absolute detection accuracy, but can improve detection performance
No
Basic settings
ModeCPU

...

 

Note
titleAttention!


Nvidia GPU 0
Nvidia GPU 1
Nvidia GPU 2
Nvidia GPU 3
Intel GPU
Huawei NPU
Sensitivity 33

Specify the sensitivity of the detector empirically. The value must be in the range [1; 99]. The default value is 33

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. The preview window displays the sensitivity scale of the

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detector that relates to the sensitivity parameter. If the scale is green,

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fire isn't detected. If the scale is yellow,

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fire

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is detected, but not enough to

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generate an event. If the scale is red,

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fire

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is detected, and the

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detector generates an event, if the scale is red through the sampling period (50 seconds by default

...

).


Example. The sensitivity parameter value of 40

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means that the

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detector will generate an event when the scale has at least

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four divisions full over the entire detection

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period. An event will end when the scale has less than

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two divisions full over the detection

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period. The

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detector will

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generate an event again if the scale has at least

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four divisions full over the entire detection

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period

By default, the entire frame is the detection

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area. In the preview window, you can

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specify the detection areas using the anchor points

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Image Added (see Configuring a detection area):

  1. Right-click anywhere in the preview window.Image Removed
  2. Select If you want to specify the detection area by one or more rectangles, select Detection area (rectangle) to set one or several rectangular areas. If you specify a rectangular area, the detection tool detector will work only within its limitsanalyze only this area. The rest of the FOV frame will be ignored.
    Select Image Added
  3. If you want to specify the detection area by one or more polygons, select Detection area (polygon) to set one or several polygonal areas. If you specify one or several polygonal areas, the detection tool detector will process analyze the entire FOV while the remaining frame. The part of the FOV frame not included in the specified polygons will be blacked out.
    Image Modified
    Note
    titleAttention!

    You

    can configure detection areas similarly to privacy masks in Scene analytics detection tools (see Setting General Zones for Scene analytics detection tools).You can use trial and error method to decide which type of detection area (rectangular or polygonal) is more effective in your case. Some neural networks give better detection with rectangles while others are better with polygons

    must select the detection area (polygon or rectangle) experimentally. For some neural networks the quality of detection will be better with rectangle, for otherswith polygon.

Info
titleNote
  • For convenience of configuration, you can "freeze" the frame. Click the Image Added button. To cancel the action, click this button again.
  • To hide detection area, click the Image Added button. To cancel the action, click this button again.
  • To delete the selected area, click the Image Added button.

To save the parameters of the detector, click the Apply Image Added button. To cancel the changes, click the Cancel Image Added button.