We use our VDE(ODD) model. Our purpose is that predict the distance between car based on Deep-Learning.

(before name) Object-Depth-detection-based-hybrid-Distance-estimator (Called, ODD // Download Link)
We will more update the github readme.

Bounding box and Depth are extracted from image data to predict the distance.

Now, we also called 'VDE'.

We use KITTI datasets. But we did not use the original data, We reconstructed the data to suit our purpose.

• Train data (# number of Data: 21,616)

• Test data (# number of Data: 2,703)

More detail performace, you can find our paper.

Quick download about preprocessing_dataset.

1. Download Dataset You can download the KITTI Data.
   Then, You unzip the data, and set the path.

os.makedirs('./data/', exist_ok=True)  
'./datasets/data'  

2. Unzip Dataset In the unzip folder, there is 'image' folder. So you move the folder into the 'data' folder.

data
├── image                    
│   ├── test
|       ├── 000000.png            
│       ├── 000001.png
│       └── ...
│   ├── train             
│       ├── 000000.png             
│       ├── 000001.png            
│       └── ...                 

3. Make Our Datasets
   We reconstructed data, because our final model, ZLE, use depth value of GLP-depth and bounding box of DETR. Apply the code below in order.

1) kitti_detr_dataset_iou.py
2) kitti_glpdepth_dataset_iou.py
# You must check the saving file name.

4. Split data

'./datasets/train_test_split.py'
# You must check the saving file name.

Look at the 'odd' folder, there are so many method, for example, LSTM, RandomForest, XGBoost.

Use the file.

weights
├── ODD_variable16.pth               
└── lstm_scaler.pth               

# Before, implementing file, you need some file below.
# :> Model weight(.pth file), scaler file
# You can download in 'Performace block'.

# And, implementing this file.
LSTM: ODD_application_LSTM.py
XGBoost: ODD_application.py

DETR
GLP-depth
huggingface-transformers
KITTI dataset

Thank you so much for your interest in our model.

we will make it.