liuruijin17/LICA

This is an official repository of LICA: A Lightweight Lane Shape Detector with Curvature-Aware Learning

LICA: A Lightweight Lane Shape Detector with Curvature-Aware Learning

• ⚡⚡ Super lightweight: The number of model parameters is 267,867 (< LSTR's 765,787).
• ⚡⚡ Super low complexity: The number of MACs (1 MAC = 2 FLOP) is only 486.617M (< LSTR's 574.280M).
• 😎 Learning structures with large curvatures without expensive, dense, and precise human annotations (predicting curved lanes better than LSTR).

Demo

The LLAMAS dataset paper defined and required lane categories when evaluating algorithms. l0 means the closest left lane to the host-vehicle, r0 means the closest right lane (likewise for l1 and r1).

In these demo videos, no tracking module is used.

Comparison between LICA and LI.

The LI is trained without the proposed curvature-aware learning.

Comparison between LICA and LSTR.

Model Zoo

The pretrained models are stored in LICAZoos/

Set Envirionment

• Linux ubuntu 16.04
• GeForce RTX 3090
• Python 3.8.5
• CUDA 11.1

Create virtualenv environment

python3 -m venv lica

Activate it

source lica/bin/activate

Then install dependencies

pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

Data Preparation

Download and extract TuSimple, and LLAMAS.

We expect the directory structure to be the following:

lica/
LICA/
    LICA/
    LICAZoos/
TuSimple/
    LaneDetection/
        clips/
        label_data_0313.json
        label_data_0531.json
        label_data_0601.json
        test_label.json
LLAMAS/
    color_images/
    labels/

Evaluation

TuSimple:

LICA(TR2)

python test.py LICA_TR2_TUSIMPLE --testiter 500000

LICA(TR6)

python test.py LICA_TR6_TUSIMPLE --testiter 500000

LLAMAS:

LICA(TR2)

python test.py LICA_TR2_LLAMAS --testiter 500000 --split validation

Training

Corresponding codes will be released after acceptance.

Acknowledgements

LSTR