Revision as of 18:53, 19 April 2023

General Information

Free, open-source based library of pre-trained AI applications available on github.
⭐Source Code: https://github.com/Ignitarium-Renesas/RZV2L_AiLibrary
This Library has API functions for leveraging AI applications that will run on Renesas RZ/V2L Board. Currently this library has following sample applications:
- Human Head Counter
- Line crossing object Counter
- Elderly people fall detection (Work in progress)
- Safety helmet and vest detection
- Human age and gender detection (Work in progress)
- Face recognition and spoof detection (Work in progress

Addition Notes

The Pre-trained models include pre-compiled applications as well as AI Models translated to run on the DRP-AI hardware. These files are located in the "exe" folder for each Pre-trained Application. These precompiled application are compiled for the Renesas RZV2L EVK using the Coral MIPI Camera. This folder can be simple copied to the board using SCP recursive command. NOTE : Some Pre-trained Applicatino

Support USB Camera

By default the Pretrained Applications are compiled to use the MIPI camera. The Pre-trained applications can be modified to use USB camera. This modification is only relevant to applications that support video.

Open the application folder src folder. For example 01_Head_count/Head_count_cam/src
Open the define.h header file.
Find the following line. Comment out the macro that defines INPUT_CORAL.

/* Coral Camera support */
#define INPUT_CORAL

Sample Videos

Head Count Application Model : YoloV3 Memory Usage: 235MB Inference Input Shape : 416,416,3	Head Count
Line Crossing Object Counting Model : YoloV3 Memory Usage: 235MB Inference Input Shape : 416,416,3	Line Count
Fall Detection Model : YoloV3 Memory Usage: 235MB Inference Input Shape : 416,416,3	Fall Detection
Age and Gender Detection Model : YoloV3 Memory Usage: 235MB Inference Input Shape : 416,416,3	Age and Gender Detection
Face Recognition, Spoofing, and Registration Model : YoloV3 Memory Usage: 235MB Inference Input Shape : 416,416,3	Face Recognition

@@ Line 28: / Line 28: @@
 {| style="width: 100%; border-style: none;"
 |- style="vertical-align: top;"
+|  <br>
+=== Head Count Application ===
+ <br> Model : YoloV3 <br> Memory Usage: 235MB <br> Inference Input Shape : 416,416,3
 |  <br> <youtube width="480">YNcCCiSx9YM</youtube> <br> '''Head Count'''
+|- style="vertical-align: top;"
+|  <br>
+=== Line Crossing Object Counting ===
+ <br> Model : YoloV3 <br> Memory Usage: 235MB <br> Inference Input Shape : 416,416,3
 |  <br> <youtube width="480" >-fZypjgsBYo</youtube> <br> '''Line Count'''
-|-
+|- style="vertical-align: top;"
+|  <br>
+=== Fall Detection ===
+ <br> Model : YoloV3 <br> Memory Usage: 235MB <br> Inference Input Shape : 416,416,3
 |  <br> <youtube width="480" >4ALde_vP1lo</youtube> <br> '''Fall Detection'''
+|- style="vertical-align: top;"
+|  <br>
+=== Age and Gender Detection ===
+ <br> Model : YoloV3 <br> Memory Usage: 235MB <br> Inference Input Shape : 416,416,3
 |  <br> <youtube width="480" >-DpAGb7q4pM</youtube> <br> '''Age and Gender Detection'''
-|-
+|- style="vertical-align: top;"
+|  <br>
+=== Face Recognition, Spoofing, and Registration ===
+ <br> Model : YoloV3 <br> Memory Usage: 235MB <br> Inference Input Shape : 416,416,3
 |  <br> <youtube width="480" >BOFdP1u-L7k</youtube> <br> '''Face Recognition'''
 |}