github-lanenet-lane-detection学习

https://github.com/MaybeShewill-CV/lanenet-lane-detection

Use tensorflow to implement a Deep Neural Network for real time lane detection mainly based on the IEEE IV conference paper “Towards End-to-End Lane Detection: an Instance Segmentation Approach”.You can refer to their paper for details https://arxiv.org/abs/1802.05591. This model consists of a encoder-decoder stage, binary semantic segmentation stage and instance semantic segmentation using discriminative loss function for real time lane detection task.

Installation

This software has only been tested on ubuntu 16.04(x64), python3.5, cuda-9.0, cudnn-7.0 with a GTX-1070 GPU. To install this software you need tensorflow 1.12.0 and other version of tensorflow has not been tested but I think it will be able to work properly in tensorflow above version 1.12. Other required package you may install them by

pip3 install -r requirements.txt

Test model

In this repo I uploaded a model trained on tusimple lane dataset Tusimple_Lane_Detection. The deep neural network inference part can achieve around a 50fps which is similar to the description in the paper. But the input pipeline I implemented now need to be improved to achieve a real time lane detection system.
在这个仓库中，我上传了一个在tusimple车道数据集Tusimple_Lane_Detection上训练的模型。 深度神经网络推理部分可以达到大约50fps，这与本文中的描述类似。 但是我现在实现的输入管道需要改进，以实现实时车道检测

tusimple车道数据集：https://github.com/TuSimple/tusimple-benchmark/issues/3

The trained lanenet model weights files are stored in new_lanenet_model_file. You can download the model and put them in folder model/tusimple_lanenet_vgg/

You can test a single image on the trained model as follows

python tools/test_lanenet.py 
--weights_path /PATH/TO/YOUT/CKPT_FILE_PATH 
--image_path ./data/tusimple_test_image/0.jpg

for example:

python tools/test_lanenet.py 
--weights_path model/New_Tusimple_Lanenet_Model_Weights/tusimple_lanenet_vgg.ckpt 
--image_path data/tusimple_test_image/0.jpg

If you want to evaluate the model on the whole tusimple test dataset you may call

python tools/evaluate_lanenet_on_tusimple.py 
--image_dir ROOT_DIR/TUSIMPLE_DATASET/test_set/clips 
--weights_path /PATH/TO/YOUT/CKPT_FILE_PATH 
--save_dir ROOT_DIR/TUSIMPLE_DATASET/test_set/test_output

If you set the save_dir argument the result will be saved in that folder or the result will not be saved but be displayed during the inference process holding on 3 seconds per image. I test the model on the whole tusimple lane detection dataset and make it a video. You may catch a glimpse of it bellow.

Train your own model

Data Preparation

Firstly you need to organize your training data refer to the data/training_data_example folder structure. And you need to generate a train.txt and a val.txt to record the data used for training the model.

The training samples are consist of three components. A binary segmentation label file and a instance segmentation label file and the original image. The binary segmentation use 255 to represent the lane field and 0 for the rest. The instance use different pixel value to represent different lane field and 0 for the rest.

All your training image will be scaled into the same scale according to the config file.

Use the script here to generate the tensorflow records file

python tools/make_tusimple_tfrecords.py 

Train model

In my experiment the training epochs are 80010, batch size is 4, initialized learning rate is 0.001 and use polynomial decay with power 0.9. About training parameters you can check the global_configuration/config.py for details. You can switch --net argument to change the base encoder stage. If you choose --net vgg then the vgg16 will be used as the base encoder stage and a pretrained parameters will be loaded. And you can modified the training script to load your own pretrained parameters or you can implement your own base encoder stage. You may call the following script to train your own model

python tools/train_lanenet_tusimple.py 

You may monitor the training process using tensorboard tools

During my experiment the Total loss drops as follows:

Training loss

The Binary Segmentation loss drops as follows:
Training binary_seg_loss

The Instance Segmentation loss drops as follows:
Training instance_seg_loss
Experiment

The accuracy during training process rises as follows: Training accuracy

Please cite my repo lanenet-lane-detection if you use it.

Train your own model

Data Preparation

Firstly you need to organize your training data refer to the data/training_data_example folder structure. And you need to generate a train.txt and a val.txt to record the data used for training the model.

The training samples are consist of three components. A binary segmentation label file and a instance segmentation label file and the original image. The binary segmentation use 255 to represent the lane field and 0 for the rest. The instance use different pixel value to represent different lane field and 0 for the rest.

All your training image will be scaled into the same scale according to the config file.

Use the script here to generate the tensorflow records file

python data_provider/lanenet_data_feed_pipline.py 
--dataset_dir ./data/training_data_example
--tfrecords_dir ./data/training_data_example/tfrecords

Train model

In my experiment the training epochs are 80010, batch size is 4, initialized learning rate is 0.001 and use polynomial decay with power 0.9. About training parameters you can check the global_configuration/config.py for details. You can switch --net argument to change the base encoder stage. If you choose --net vgg then the vgg16 will be used as the base encoder stage and a pretrained parameters will be loaded. And you can modified the training script to load your own pretrained parameters or you can implement your own base encoder stage. You may call the following script to train your own model

python tools/train_lanenet.py 
--net vgg 
--dataset_dir ./data/training_data_example
-m 0

You can also continue the training process from the snapshot by

python tools/train_lanenet.py 
--net vgg 
--dataset_dir data/training_data_example/ 
--weights_path path/to/your/last/checkpoint
-m 0

You may monitor the training process using tensorboard tools

During my experiment the Total loss drops as follows:
Training loss

The Binary Segmentation loss drops as follows:
Training binary_seg_loss

The Instance Segmentation loss drops as follows:
Training instance_seg_loss

Recently updates 2018.11.10

Adjust some basic cnn op according to the new tensorflow api. Use the traditional SGD optimizer to optimize the whole model instead of the origin Adam optimizer used in the origin paper. I have found that the SGD optimizer will lead to more stable training process and will not easily stuck into nan loss which may often happen when using the origin code.

I have uploaded a new lanenet model trained on tusimple dataset using the new code here new_lanenet_model_file. You may download the new model weights and update the new code. To update the new code you just need to

git pull origin master

The rest are just the same as which mentioned above. And recently I will release a new model trained on culane dataset.

Recently updates 2018.12.13

Since a lot of user want a automatic tools to generate the training samples from the Tusimple Dataset. I upload the tools I use to generate the training samples. You need to firstly download the Tusimple dataset and unzip the file to your local disk. Then run the following command to generate the training samples and the train.txt file.

python tools/generate_tusimple_dataset.py --src_dir path/to/your/unzipped/file

The script will make the train folder and the test folder. The training samples of origin rgb image, binary label image, instance label image will be automatically generated in the training/gt_image, training/gt_binary_image, training/gt_instance_image folder.You may check it yourself before start the training process.

Pay attention that the script only process the training samples and you need to select several lines from the train.txt to generate your own val.txt file. In order to obtain the test images you can modify the script on your own.

Recently updates 2019.05.16

New model weights can be found here
Recently updates 2020.06.12

Add real-time segmentation model BiseNetV2 as lanenet backbone. You may modify the config/tusimple_lanenet.yaml config file to choose the front-end of lanenet model.

New lanenet model trainned based on BiseNetV2 can be found here

The new model can reach 78 fps in single image inference process.

MNN Project

Add tools to convert lanenet tensorflow ckpt model into mnn model and deploy the model on mobile device
Freeze your tensorflow ckpt model weights file

cd LANENET_PROJECT_ROOT_DIR
python mnn_project/freeze_lanenet_model.py -w lanenet.ckpt -s lanenet.pb

Convert pb model into mnn model

cd MNN_PROJECT_ROOT_DIR/tools/converter/build
./MNNConver -f TF --modelFile lanenet.pb --MNNModel lanenet.mnn --bizCode MNN

Add lanenet source code into MNN project

Add lanenet source code into MNN project and modified CMakeList.txt to compile the executable binary file.