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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 detector.

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

4. In the Track retention time (s) field, enter the time in seconds in the range from 0.3 to 1000 after which the object track is considered lost. You can use this parameter in situations when one object in the frame temporarily overlaps another.

5. By default, the One event per equipment element checkbox is set, and the detector generates an event once for each equipment element violation within an object (track). If you want the detector to generate an event 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 is one event, if not—two not, two events.

6. Set the Show objects on image checkbox if it is necessary to outline the detected object with a border on the image in the Monitor interface object window.

7. 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.

8. 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 can take several minutes to launch the algorithm on the 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 in Windows OS). If you specify another processing resource than the CPU, this device will carry most of the computing load. However, the CPU will also be used to run the detector.

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

Selecting the area of interest

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1. Go to the Network settings tab on the settings panel of the detector.
   
2. By default, the standard (default) segmenting neural network is initialized according to the device selected in the Working mode drop-down list. The standard neural networks for different processor types are selected automatically. If you use a custom segmenting neural network, click the  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: classification neural network (PPE on the head) and classification neural network (PPE on the body) according to the selected processing device in the Working mode drop-down list. Each classification neural network detects equipment on a specific body segment. The standard classification neural networks for different processor types are 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.

The Configuring the Equipment detection (PPE)module is now configuredcomplete.