3.Basic Settings

(1)
Select [AI On-site Learning Management] from the submenu.
(2)
Check the checkbox for the camera for which you want to configure this product.
This product is set by one camera. Check only one check box on the camera.
(3)
Select [The entire process is carried out in a camera-connected environment].
(4)
Click [Display].
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[AI On-site Learning Settings] is displayed on a separate screen.
(5)
Follow the guidance to make basic settings for the product
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Each item has a symbol. Click this symbol to display the procedure in the lower left corner of the screen.
Detector 1 is set on the Guidance screen.

First, select the [On-site Learning] function you want to use..
This product provides the following three types of [On-site Learning] function.
[Adding a new detection object]
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Adds a new Detection Object.
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The object name can be set in [Set Name].
[Improving false detection]
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Prevents false detection of humans, vehicles, or two-wheeled vehicles.
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The target for improvements can be selected from [human], [vehicle] and [bicycle].
[Improving missed detection]
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Reduces missed detection of humans, vehicles, or two-wheeled vehicles.
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The target for improvements can be selected from [human], [vehicle] and [bicycle].
Save the image to be used for learning.

You can save images directly from a pre-installed camera or from a JPEG/PNG file on your PC as learning images.

To take pictures directly from a pre-installed camera, select [Manually save camera image] or [Auto save camera image].
[Manually save camera image] allows you to manually save still images each time.
In [Auto save camera image], when you specify [Save interval] and [Number of save images], the camera automatically saves one still image for [Number of save images] at the specified [Save interval].

Select [Upload images from PC] to save jpg/png files on your computer as learning images.
1.
Select [Saving learning images].
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Select [Manually save camera image].
2.
Click the save button.
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One still image is shot.
1.
Select [Saving learning images].
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Select [Auto save camera image].
2.
Set the [Image saving interval].
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Select from [10 s], [20 s], [30 s], [40 s], [50 s], [1min], [5min], [10min], [15min], [30min], [60min], and [When nothing detected].
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When [When nothing detected] is selected, only images that could not be detected are efficiently saved.
3.
Select [Number of save images]
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Select from [10], [20],..., [90], [100], [150], and [200].
4.
Click [Start]
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To stop auto saving, click [Stop].
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During auto save, the progress is displayed beside the [Stop].
1.
Select [Saving the learning image].
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Select [Upload images from PC].
2.
Click [Browse].
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The Add File screen is displayed.
3.
Select the files you want to save as learning images, and then click [Open].
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Saving starts.
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The progress is displayed while the file is being saved.

・The learning image formats are compatible with.jpg and.png.


・The resolution of the image that can be saved as a learning image is between 640pixels and 3840pixels.


・Up to 1000 learning images can be saved. However, depending on the resolution of the learning image, you may not be able to save up to 1,000 images.


・The percentage usage of the storage area for learned images can be checked on [Maintenance] window.


4.4 Maintenance
You can draw the bounding box you want the AI to learn in the learning image.
1.
Click [Display]
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[Perform Learning Screen] is displayed.
2.
Click the thumbnail of image you want to use to set the bounding box.
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[Learning Frame Settings Screen] is displayed.
When [Displays the Detection frame for Humans,Vehicles, and Bicycles] is selected, the detection results for human/vehicle/bicycle are displayed in gray. The detection result of human, vehicle, or bicycle exceeds the detection threshold for human, vehicle, or bicycle. See below for detection thresholds.
5. Demo Screen
3.
Select [Add Bounding box] in [Mode] to draw a bounding box on the image.
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You can set the learning frame as a rectangle by dragging it on the image.
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Select [Edit Bounding box] in [Mode] and click the drawn bounding box to change the size and position of the bounding box or to delete it by clicking the × icon.
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Clicking the Icons for the detection results (gray frames) of human/vehicle/bicycle will set the selected detection object to the bounding box.

・If the size of the learning frame you draw is too small or too large, the size is automatically adjusted. See the following for the minimum and maximum sizes.


1.3 Camera Installation Conditions

・The detection result of human/vehicle/bicycle is displayed by checking [Displays the Detection Frame for Humans, Vehicles, and Bicycles.] in the [Perform Learning Screen].


・You can set up to 100 bounding box per image.


4.
Press [Next] to set the bounding box for the remaining images.
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The setting screen for the next image is displayed.
5.
When you have set all the learning images, click [Set].
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[Learning Frame Setting Screen] closes, and you sre returned to the [Perform Learning Screen].
The [Set bounding boxes and learn] process can be interrupted halfway and continued in the camera unconnected environment. See the chapter below.
7.2 Stop the work in a camera-connected environment and continue in a non-camera environment
6.
Select the image you want to use for learning
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Select the check box at the bottom left of the thumbnails of the target images as shown.
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Learning requires at least 10 images.You can learn up to 200 images.
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The maximum number of bounding boxes that can be included in learning images are 1000.
When bounding boxes are set, the check boxes at the bottom left of the thumbnails are automatically checked.
7.
Click the [Start Learning] to execute learning.
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When learning is completed, the confirmation screen is displayed.
Check the learning accuracy.
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To check the image with live video, click [Display] on [Demo screen] to open the demonstration screen. See the following for information on the demonstration screen.
5. Demo Screen
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To check images already shot, click [Display] on [Simulation screen] to open the simulation screen. See the following for more information on the simulation screen.
6. Simulation screen