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Tip

Camera requirements for Fire Detection

Hardware requirements for neural analytics operation

To configure smoke (fire) detection tool:

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the Fire Detection, do the following:

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

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

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

ParameterValueDescription
Object features
Record mask to archiveYesBy default, the sensitivity scale of the detection tool is recorded to the archive (

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see Displaying information from a detection tool (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 detection tool is enabled by default. To disable the detection tool, select the No value
No
NameFire DetectionEnter the detection tool name or leave the default name
Decoder modeAutoSelect a processing resource for decoding video streams

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. 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 detection tool

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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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titleAttention!
Fire DetectionName of the detection tool type (non-editable field)
Advanced settings
Neural network file

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Select a neural network file

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. 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

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Info
titleNote

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

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

Specify

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the minimum number of frames with

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fire

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for

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the detection tool

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to trigger. The value

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must be in the range [5; 20]

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Scanning modeYesThe parameter is enabled by default. You can use the scanning mode (see Scanning mode in Axxon One) 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

Select the processor for the neural network—CPU, one of NVIDIA GPUs or one of Intel GPUs (seeHardware requirements for neural analytics operation, General information on configuring detection). 

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.

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

Specify

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the sensitivity of the

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detection tool empirically. The value

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must be in the range [1; 99]

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. The default value is 33. The preview window displays the sensitivity scale of the detection tool 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 trigger the tool. If the scale is red,

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fire

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is detected and the detection tool will trigger, if the scale is red through the sampling period (50 seconds by default

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).


Example. The sensitivity parameter value of 40

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means that the

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detection tool will trigger when the scale has at least

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four divisions full over the entire detection

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period. The triggering will stop 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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detection tool will trigger 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:

  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 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 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

    specify the detection areas similarly to

    privacy masks in

    the excluded areas of the Scene analytics detection tools (see

    Setting General Zones for Scene analytics detection tools

    Configuring the Detection Zone).

    You can use trial and error method to decide which type of

    You must select the detection area (

    rectangular

    polygon or

    polygonal) is more effective in your case. Some neural networks give better detection with rectangles while others are better with polygons

    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 detection tool, click the Apply Image Added button. To cancel the changes, click the Cancel Image Added button.