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license: mit
base_model: cfzd/Ultra-Fast-Lane-Detection
tags:
- tflite
- lane-detection
- object-detection
- quantized
- android
- automotive
- autonomous-driving
- adas
language:
- en
pipeline_tag: object-detection
---
# Car Nebula ADAS — On-Device TFLite Models
Two TFLite models used by [Car Nebula](https://carnebula.app) for real-time Advanced Driver
Assistance (ADAS) running entirely on-device on Android automotive hardware.
---
## Models
### 1. `lane_detector.tflite` — Lane Detection
| Property | Value |
|---|---|
| **Base model** | Ultra-Fast Lane Detection (cfzd/Ultra-Fast-Lane-Detection) |
| **Architecture** | ResNet-18 backbone |
| **Dataset** | TUSimple (highway lanes, US dashcam footage) |
| **Original weights** | `tusimple_res18.pth` (official pre-trained) |
| **Input shape** | `[1, 288, 800, 3]` — float32 or int8, NHWC |
| **Input normalization** | ImageNet: mean `[0.485, 0.456, 0.406]`, std `[0.229, 0.224, 0.225]` |
| **Output shape** | `[1, 201, 56, 4]` or `[1, 4, 56, 201]` |
| **Output format** | 201 grid bins (200 x-positions + 1 no-lane) × 56 row anchors × 4 lanes |
| **License** | MIT |
**Conversion pipeline:**
```
tusimple_res18.pth → ONNX (opset 11) → TF SavedModel → TFLite
```
Conversion script: `convert_ufld.py` (included in the Car Nebula Android repo).
**Row anchors (TUSimple, 56 rows, pixel Y in 288-px input):**
```
64 68 72 76 80 84 88 92 96 100 104 108 112 116 120 124 128 132 136 140
144 148 152 156 160 164 168 172 176 180 184 188 192 196 200 204 208 212
216 220 224 228 232 236 240 244 248 252 256 260 264 268 272 276 280 284
```
---
### 2. `efficientdet_lite0.tflite` — Object Detection
| Property | Value |
|---|---|
| **Model family** | EfficientDet Lite0 |
| **Source** | [TensorFlow Hub](https://tfhub.dev/tensorflow/lite-model/efficientdet/lite0/detection/metadata/1) |
| **Input shape** | `[1, 320, 320, 3]` — uint8, NHWC |
| **Output** | Bounding boxes · class scores · class labels · detection count |
| **Classes** | 90 COCO classes |
| **Score threshold** | 0.38 (used by Car Nebula pipeline) |
| **License** | Apache 2.0 |
---
## How the pipeline uses both models
```
Camera frame (CameraX / USB UVC)
│
├──► EfficientDet Lite0 (every 3 frames)
│ └──► AdasBox list: label, bounding rect, estimated distance
│ └──► HUD overlay: boxes, collision warning, top-down view
│
└──► UFLD ResNet-18 (every 3 frames)
└──► Lane boundary points (left/right, top/bottom)
└──► Camera overlay: seg mask, lane lines
HUD: road corridor, departure warning
```
Results are temporally smoothed between inference runs so the UI always has
something to render even on frames that skip inference.
---
## Usage (Android / TFLite Java API)
```kotlin
// Load from downloaded file
val model = FileInputStream(file).channel.use { ch ->
ch.map(FileChannel.MapMode.READ_ONLY, 0, file.length())
}
val interpreter = InterpreterApi.create(
model,
InterpreterApi.Options()
.setRuntime(InterpreterApi.Options.TfLiteRuntime.FROM_SYSTEM_ONLY)
.setNumThreads(2)
)
```
---
## Licenses
| Model | License |
|---|---|
| `lane_detector.tflite` (UFLD ResNet-18) | [MIT](https://github.com/cfzd/Ultra-Fast-Lane-Detection/blob/master/LICENSE) |
| `efficientdet_lite0.tflite` (TFHub) | [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
|