TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars

Requirement

See requirements.txt for additional dependencies and version requirements.

pip install -r requirements.txt

Data Preparation

• Download the images from images.

• Download the annotations of drivable area segmentation from segments.

• Download the annotations of lane line segmentation from lane.

/data
    bdd100k
        images
            train/
            val/
            test/
        segments
            train/
            val/
        lane
            train/
            val/

Pipeline

Train

python3 main.py

Test

python3 val.py

Inference

Images

python3 test_image.py

Visualize

Drive-able segmentation

### Lane Detection

Acknowledgement

Our source code is inspired by:

• ESPNet
• YOLOP

Citation

If you find our paper and code useful for your research, please consider giving a star ⭐ and citation 📝 :

@INPROCEEDINGS{10288646,
  author={Che, Quang-Huy and Nguyen, Dinh-Phuc and Pham, Minh-Quan and Lam, Duc-Khai},
  booktitle={2023 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)}, 
  title={TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars}, 
  year={2023},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/MAPR59823.2023.10288646}}

TwinLiteNetV2: A small stone can kill a giant

🚀 Coming soon!

Model	size (Height x Width)	Lane (Accuracy)	Lane (IOU)	Drivable Area (mIOU)	params (M)	FLOPs (B)
TwinLiteNetV2-Nano	384 x 640	70.8	23.6	87.2	0.03	0.485
TwinLiteNetV2-Small	384 x 640	75.9	28.7	90.4	0.14	1.366
TwinLiteNetv2-Medium	384 x 640	79.3	32.6	92.3	0.62	5.088
TwinLiteNetV2-Large	384 x 640	81.7	34.2	92.9	2.78	21.526