Vehicle App
App Overview
Detects vehicles and classifies vehicle types. The classification function will be updated soon.
Main Features
- Detects vehicles and classifies vehicle types.
- Events are generated by checking for reverse driving or speeding.
App Application Architecture
Architecture Overview
Det (Vehicle)─────Tracker (NvDCF)
VehicleDet : {0:vehicle}
Model Infomation
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VehicleDet
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Description
Detects vehicles.
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Version
v1.0.0 -
Dataset Properties
Category name Number of Images Number of Instances vehicle 23287 194050 Total 23287 194050 -
Performance
Category name Recall 50:95 Precision 50:95 vehicle 86 93.1 mean 86 93.1
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Input / Output
Input
Input Type : Image
Input Format : RGB
Input Resolution : 640 X 640 X 3 (W x H x C)
INPUT IMAGE
(picture)
Output
Output Type : Lable, Boundary-box(Bbox), Confidence Scores, Track ID
| Output Type | Data Type |
|---|---|
| Lable | str |
| Bbox | list: [x: float, y:float, w:float , h:float ] |
| Confidence Scores | float |
| Track ID | int |
| Event | str |
OUTPUT IMAGE (picture)
Software Integration
Runtime Engine:
- Autocare Edge 1.7
Preferred System Spec:
- OS : Ubuntu (debian)
- GPU : NVIDIA RTX 3070, NVIDIA RTX 4090, etc(Nvidia).
Limitation & Warning
Small objects : The object size must be at least 5% of the total image size for detection.
Crowded objects : In the case of crowd objects, they can be recognized as a single object by the Iou algorithm.
Camera distortion : A skewed input may not fit the model's domain.
Dark or blurry objects : It may not pass the threshold and therefore go undetected.