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Exploring the use of bezier curves for lane detection —— A baseline model.

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BezierLaneNet

Exploring the use of bezier curves for lane detection —— A baseline model.

Paper

Still Work In Progress...

Dataset

Download CULane dataset here.

Installation

  1. install PyTorch;
  2. run the following command:
pip3 install -r requirements.txt

Training

Command-line arguments to reproduce the result:

python3 train.py --data ./CULane --log_name baseline --pretrained_weight ./weights/resnet18-5c106cde.pth --input_size 820 295 --gpu_ids 0

Note: make sure that you've downloaded the weight for ResNet18 pre-trained on ImageNet.

Inference

Run the following command:

python3 inference.py --data ./CULane --ckpt [Path to the model checkpoint file] --input_size 820 295 --gpu_ids 0 --save_name "./video/output.avi"

Possible Directions for Improvement

A. Pay More Attention to the curved lanes:

  1. Use Focal loss;
  2. Adaptive weighting according to the curvature;
  3. Data resampling;
  4. ......

B. Apply Data Augmentation

C. Try Using Different Loss Function

D. Incorporate with Temporal Information:

  1. Training on Video Data;
  2. Use Consistent Loss;
  3. ......

E. ......

And Let Me Know If You Have Any Progress on These Directions!!!

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Exploring the use of bezier curves for lane detection —— A baseline model.

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