License Plate Recognition
Visionaire License Plate Recognition (Docker Stream) is a containerized Artificial Intelligence Analytics which provides recognition of license plates for all types of vehicles. Nodeflux LPR is using a Deep Learning algorithm developed to adapt to real conditions in Indonesia, like various kinds of plates, and any wide range of environmental conditions.
How it Works
Camera Specification
Camera Settings | License Plate Recognition | General Analytics |
---|---|---|
Frame Resolution | 1080p - 4K | 720p - 1080p |
Codec | H.264 | H.264 |
Codec Profile | Main | Baseline |
Bit Rate Mode | Constant (CBR) | Variable (VBR) |
Target Bitrate | 4 - 10 Mbps | 1 - 4 Mbps |
FPS Rate | 30 | 15 - 30 |
Bandwidth | 6 Mbps | 4 Mbps |
Exposure Time/Shutter Speed | 1/1000s - 1/2000s | 1/30s - 1/120s |
Deployment Schema
Analytics Performance
LPRv2
Parameter | Value |
---|---|
Character Accuracy | 98.71% |
Plate Type | Military, Police, Diplomat, Public (Only Indonesia) |
Weather Condition | Rainy, Dusty, Visible Low-Light, Daylight |
Minimum Plate Size | 250px x 150px |
Maximum Tilt | 30° |
Plate Condition | Bended, Cropped, Framed |
LPR-S (Special Case)
Parameter | Value |
---|---|
Character Accuracy | 80% |
Plate Type | Indonesian Military (TNI AD, AL, AU), Mabes TNI, Police, Private |
Weather Condition | Rainy, Dusty, Visible Low-Light, Daylight |
Minimum Plate Size | 250px x 150px |
Maximum Tilt | 30° |
Plate Condition | Bended, Cropped, Framed |
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