Documentation for Axxon Next 4.5.0 - 4.5.10. Documentation for other versions of Axxon Next is available too.

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Camera requirements for Fire and Smoke Detection

To configure smoke (fire) detection tool:

  1. To record the sensitivity scale to archive, set Yes for  the corresponding parameter (1).

  2. If a camera supports multistreaming, select the stream for which detection is needed (2). Selecting a low-quality video stream allows reducing the load on the Server.
  3. Select a processing resource for decoding video streams (3). When you select a GPU, a stand-alone graphics card takes priority (when decoding with NVidia NVDEC chips). If there's no appropriate GPU, the decoding will use the Intel Quick Sync Video technology. Otherwise, CPU resources will be used for decoding.

  4. Set the frame rate value for the detection tool to process (4). The value should be in the [0,016, 100] range.

    Note

    The default values (5 frames and 0.1 fps) indicate that the tool will analyze frame over 50 seconds span.  The detection tool samples 1 frame per 10 seconds. If it detects smoke/fire on 5 consecutive fames, it triggers an alert.

  5. Select the processor for the neural network — CPU, one of GPUs or a IntelNCS (5). 

    Attention!

    It may take several minutes to launch the algorithm on an NVIDIA GPU after you apply the settings. You can use caching to speed up future launches (see Configuring the acceleration of GPU-based neuroanalytics).

    Attention!

    If you specify other processing resource than the CPU, this device will carry the most of computing load. However, the detection tool will consume CPU as well.

  6. Select a neural network file (6).  The following standard neural networks for different processor types are located in C:\Program Files\Common Files\AxxonSoft\DetectorPack\NeuroSDK:

    smoke_movidius.ann

    Smoke detector / IntelNCS

    smoke_openvino.ann

    Smoke detector / CPU

    smoke_original.ann

    Smoke detector / GPU

    fire_movidius.ann

    Fire detector / IntelNCS

    fire_openvino.ann

    Fire detector / CPU

    fire_original.ann

    Fire detector / GPU

    Enter full path to a custom neural network file into this field. This is not required if you use standard neural networks which are selected automatically.

    Note

    For correct neural network operation under Linux, place the corresponding file in the /opt/AxxonSoft/AxxonNext/ directory. 

  7. Set the minimum number of frames with smoke (fire) for triggering the tool (7). The value should be in the [5;20] range.
  8. You can experiment with the sensitivity of the tool (8). The value must be in the range [1; 99].  The preview window displays the sensitivity scale of the detection tool that relates to the Sensitivity parameter. If the scale is green, smoke (fire) is not detected.  If the scale is yellow, smoke (fire) is detected, but not enough to trigger the tool. If the scale is red, smoke (fire) is detected and the detection tool  will trigger, if the scale is red through the sampling period (50 seconds by default, see item 4).
    Example. The sensitivity parameter value of 40 implies that the alert is triggered when the scale has been at least 4 graduations full over the entire detection time span (50 sec by default, see i.4). The triggering stops when the scale has been less than 2 graduations full during analysis time. The alert is triggered again if the scale has been at least 4 graduations full over the entire detection time span.

  9. By default, the detection is performed over full image area. In the preview window, you can set several detection zones by their anchor points as follows:

    1. Right-click anywhere in the Preview window.

    2. Select Detection area (rectangle) for a rectangular zone. If you specify a rectangular area, the detection tool will work only within its limits; the rest of the FOV will be ignored.

    3. Select Analytics Area (polygon) to set one or several polygonal zones. If you specify one or several polygonal areas, the detection tool will process the entire FOV while the remaining part of the FOV will be blacked out.

      Note

      You can configure detection zones similarly to privacy masks in scene analytics (see Setting General Zones for Scene Analytics).

Important!

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.

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