https://github.com/MaybeShewill-CV/MNN-LaneNet

Lane detection model for mobile device via MNN project
https://github.com/MaybeShewill-CV/MNN-LaneNet

deep-learning lane-detection mnn semantic-segmentation

Last synced: 16 days ago
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Lane detection model for mobile device via MNN project

• Host: GitHub
• URL: https://github.com/MaybeShewill-CV/MNN-LaneNet
• Owner: MaybeShewill-CV
• Created: 2019-11-08T13:03:28.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-14T12:16:43.000Z (over 4 years ago)
• Last Synced: 2024-08-01T03:18:54.013Z (3 months ago)
• Topics: deep-learning, lane-detection, mnn, semantic-segmentation
• Language: C++
• Size: 1.01 MB
• Stars: 74
• Watchers: 9
• Forks: 21
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# MNN-LaneNet

Lane detection model for mobile device via MNN project. Thanks for the

great efforts of [li-qing](https://github.com/li-qing) etc.

# 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.

The main network architecture is as follows:

`Network Architecture`

![NetWork_Architecture](./data/source_image/network_architecture.png)

## Installation

This project has been built and tested on Ubuntu16.04. Tests on other

platform will be done recently.

**OS**: Ubuntu 16.04 LTS

**Tensorflow**: tensorflow 1.12.0

**MNN**: mnn 0.2.1.0

#### Common Preparation

```

1.cd ROOT_DIR && git clone https://github.com/MaybeShewill-CV/MNN-LaneNet.git

2.Download the ckpt file path here https://www.dropbox.com/sh/yndoipxt6nbhg5g/AAAPxZDDO2N0HP0YonetamJoa?dl=0

and place the ckpt file into folder ./checkpoint

```

#### Convert Model File

First you need to compile your own MNNConverter tools in your local

environment. Then you're supposed to modify the script for conversion in

folder ./checkpoint convert_ckpt_into_mnn_model.sh. Run the following

commands

```

cd ROOT_DIR

bash checkpoint/convert_ckpt_into_mnn_model.sh MNNConverter_TOOL_PATH

```

You may get some useful information via following command

```

cd ROOT_DIR

bash checkpoint/convert_ckpt_into_mnn_model.sh -h

```

You will get the mnn model named lanenet_model.mnn in folder ./checkpoint

if everything works correctly

#### Build Binary file

```

1.cd ROOT_DIR/build

2.cmake .. && make -j4

```

You will get the built executable binary file named lane_detector.out in

folder ./build if everything works correctly

## Test model

Run the following command

```

cd ROOT_DIR/build

./lanenet_detector.out ./config.ini ../data/tusimple_test_image/lanenet_test.jpg

```

The results are as follows:

`Test Input Image`

![Test Input](./data/tusimple_test_image/lanenet_test.jpg)

`Test Lane Binary Segmentation Image`

![Test Lane_Binary_Seg](./data/source_image/binary_ret.png)

`Test Lane Instance Segmentation Image`

![Test Lane_Instance_Seg](./data/source_image/instance_ret.png)

## Reference

The origin lanenet repo can be found [here](https://github.com/MaybeShewill-CV/lanenet-lane-detection).

Feel free to raise issues to help the repo become better.

## TODO

- [ ] Test the model on TX2 platform

- [ ] Add time cost profile tools to evaluate the speed on different

platform

## Acknowledgement

The lanenet project refers to the following projects:

- [Tensorflow](https://github.com/tensorflow/tensorflow)

- [MNN](https://github.com/alibaba/MNN)

- [SimpleDBSCAN](https://github.com/CallmeNezha/SimpleDBSCAN)