To configure smoke (fire) detection tool:
Set the interval between the processes frames in seconds (3). The value should be in the [1;30] range.
The default values (5 frames and 10 seconds) indicate that the tool will analyze one frame every 10 seconds. When smoke (fire) is detected in 5 frames, the tool will trigger. |
Select the processor for the neural network — CPU, one of GPUs or a IntelNCS (4).
It may take several minutes to launch the algorithm on an NVIDIA GPU after you apply the settings. |
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. |
Select a neural network file (5). 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.
For correct neural network operation under Linux, place the corresponding file in the /opt/AxxonSoft/AxxonNext/ directory. |
Green - smoke (fire) not detected.
Yellow - smoke (fire) detected, but not enough to trigger the tool.
Red - smoke (fire) detected.
If you increase the sensitivity value, you have more alerts (red scale).
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:
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.
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.
You can configure detection zones similarly to privacy masks in scene analytics (see Setting General Zones for Scene Analytics). |
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. |