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1. In the Min person height, % and Min person width, % fields, enter the minimum height and width of a person in the frame as a percentage of the frame height/width. Objects smaller than the specified size will not be detected.
2. In the Frames processed per second [0.016, 100] field, set the number of frames per second that will be processed by the detection tool.

3. In the Number of frames for analysis and output field, enter the minimum number of frames on which a violation must be detected in order to generate a trigger. The value must be in the range [2; 20].

4. By default, the One event per equipment element checkbox is set, and the detection tool triggers once for each equipment element violation within an object (track). If you want the detection tool to trigger each time an equipment violation occurs, clear the checkbox.

   Info
   title Note
   Example. A person appeared in the frame without a helmet, then put it on and then took it off again. If the One event per equipment element checkbox is set, then there will be one trigger, if not—two triggers.

5. Set the Show objects on image checkbox if it is necessary to highlight the detected object with a frame on the image in the Monitor interface object window.

6. Set the Save tracks to show in archive checkbox to save the object (track) to the archive.
   

   Info
   title Note
   The frame on the image of the detected object is saved in the Monitor object archive.

   
   

7. From the Working mode drop-down list, select the device on which the neural network will operate: CPU, one of NVIDIA GPUs or one of Intel GPUs. The default value is CPU. Depending on the device that you select, the neural networks will be selected.
   

   Note
   title Attention!
   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. If you select Intel HDDL, due to the features of the device, only the segmenting neural network will be processed on it, the CPU will be used to run the classification neural networks.

    

8. Set the PPE detection checkbox to detect the presence of personal protective equipment (PPE). By default, the checkbox is clear.

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1. Go to the Network settings tab on the settings panel of the detection tool.
   
2. By default, the Segmenting standard (default) segmenting neural network is initialized according to the device selected processing device in the Working mode parameterdrop-down list. The standard segmenting You don't need to select the standard neural networks in this field, the system will automatically select the required one neural networks for different processor types are located at the path C:\Program Files (x86)\Axxon PSIM\Modules64\caffeewrapper\Models.  are selected automatically. If you use a custom segmenting neural network, click the the Image Modified button (1) to the right of the Segmenting network file field, and in the standard Windows Explorer window, specify the path to the file.
3. By default, two standard classification neural networks are initialized: сlassification neural network (equipment and PPE on the head) and сlassification neural network (equipment and PPE on the body) according to the selected processing device in the Working mode parameter drop-down list. Each classification neural network detects equipment on a specific body segment. The standard classification neural networks for different processor types are located at the path C:\Program Files (x86)\Axxon PSIM\Modules64\caffeewrapper\Models. You don't need to select the standard neural networks, the system will automatically select the required one. If selected automatically. If you want to detect only one item of equipment, click the button to the right of the Classification network file field (2), and in the standard Windows Explorer window, specify the path to the custom neural network file. If there are several custom neural network files, specify the path to each.
4. Click the Apply button to save the settings.

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