From 39e964ff1698503aeef4e6fcf6f7c0cba669b6c4 Mon Sep 17 00:00:00 2001 From: Zillur Rahman <54721606+zillur-av@users.noreply.github.com> Date: Tue, 13 Jun 2023 16:23:39 -0700 Subject: [PATCH 1/5] Update README.md --- README.md | 38 +++++++------------------------------- 1 file changed, 7 insertions(+), 31 deletions(-) diff --git a/README.md b/README.md index 4238267..6657cdc 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # LaneDet ## Introduction -LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and build their own methods. +This repository is the official implementation of the paper "LVLane: Lane Detection and Classification in Challenging Conditions", accpeted in XYZ conference. ![demo image](.github/_clips_0601_1494452613491980502_20.jpg) @@ -19,15 +19,10 @@ Supported backbones: - [x] ERFNet - [x] VGG - [x] MobileNet -- [] DLA(coming soon) Supported detectors: -- [x] [SCNN](configs/scnn) - [x] [UFLD](configs/ufld) - [x] [RESA](configs/resa) -- [x] [LaneATT](configs/laneatt) -- [x] [CondLane](configs/condlane) -- [] CLRNet(coming soon) ## Installation @@ -37,7 +32,7 @@ Please refer to [INSTALL.md](INSTALL.md) for installation. ### Clone this repository ``` -git clone https://github.com/turoad/lanedet.git +git clone https://github.com/zillur-av/LVLane.git ``` We call this directory as `$LANEDET_ROOT` @@ -64,23 +59,6 @@ python setup.py build develop ### Data preparation -#### CULane - -Download [CULane](https://xingangpan.github.io/projects/CULane.html). Then extract them to `$CULANEROOT`. Create link to `data` directory. - -```Shell -cd $LANEDET_ROOT -mkdir -p data -ln -s $CULANEROOT data/CULane -``` - -For CULane, you should have structure like this: -``` -$CULANEROOT/driver_xx_xxframe # data folders x6 -$CULANEROOT/laneseg_label_w16 # lane segmentation labels -$CULANEROOT/list # data lists -``` - #### Tusimple Download [Tusimple](https://github.com/TuSimple/tusimple-benchmark/issues/3). Then extract them to `$TUSIMPLEROOT`. Create link to `data` directory. @@ -94,12 +72,14 @@ For Tusimple, you should have structure like this: ``` $TUSIMPLEROOT/clips # data folders $TUSIMPLEROOT/lable_data_xxxx.json # label json file x4 -$TUSIMPLEROOT/test_tasks_0627.json # test tasks json file $TUSIMPLEROOT/test_label.json # test label json file ``` +### LVLane +Download [LVLane](). Then extract them to `$TUSIMPLEROOT` just like TuSimple dataset. This link contains class annotations for TuSimple dataset too, replace the orginal ones with these. + -For Tusimple, the segmentation annotation is not provided, hence we need to generate segmentation from the json annotation. +We need to generate segmentation from the json annotation. ```Shell python tools/generate_seg_tusimple.py --root $TUSIMPLEROOT @@ -177,14 +157,10 @@ This project is released under the [Apache 2.0 license](LICNESE). ## Acknowledgement -* [open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection) +* [Turoad/lanedet](https://github.com/Turoad/lanedet) * [pytorch/vision](https://github.com/pytorch/vision) -* [cardwing/Codes-for-Lane-Detection](https://github.com/cardwing/Codes-for-Lane-Detection) -* [XingangPan/SCNN](https://github.com/XingangPan/SCNN) * [ZJULearning/resa](https://github.com/ZJULearning/resa) * [cfzd/Ultra-Fast-Lane-Detection](https://github.com/cfzd/Ultra-Fast-Lane-Detection) -* 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+![demo image](.github/test-class-lvlane-ufld2.jpg) ## Table of Contents * [Introduction](#Introduction) @@ -26,6 +26,7 @@ Supported detectors: ## Installation +This repository is a modified version of [lanedet](github.com/Turoad/lanedet.git); so, it you installed that, no need to install this one. @@ -57,9 +58,9 @@ pip install torch==1.8.0 torchvision==0.9.0 python setup.py build develop ``` -### Data preparation +## Data preparation -#### Tusimple +### Tusimple Download [Tusimple](https://github.com/TuSimple/tusimple-benchmark/issues/3). Then extract them to `$TUSIMPLEROOT`. Create link to `data` directory. ```Shell From b606c95bef6ea361152fc67d19c194b38b61e4cc Mon Sep 17 00:00:00 2001 From: Zillur Rahman <54721606+zillur-av@users.noreply.github.com> Date: Mon, 19 Jun 2023 12:33:14 -0700 Subject: [PATCH 4/5] Update README.md --- README.md | 30 +++++++++++++++++++++--------- 1 file changed, 21 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 4061a4d..5d6e557 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ Supported detectors: ## Installation -This repository is a modified version of [lanedet](github.com/Turoad/lanedet.git); so, it you installed that, no need to install this one. +This repository is a modified version of [lanedet](https://github.com/Turoad/lanedet.git); so, it you installed that, no need to install this one. Just clone this and use the same conda environment. @@ -61,33 +61,45 @@ python setup.py build develop ## Data preparation ### Tusimple -Download [Tusimple](https://github.com/TuSimple/tusimple-benchmark/issues/3). Then extract them to `$TUSIMPLEROOT`. Create link to `data` directory. +Download [Tusimple](https://github.com/TuSimple/tusimple-benchmark/issues/3). Then extract them to `$DATASETROOT`. Create link to `data` directory. ```Shell cd $LANEDET_ROOT mkdir -p data -ln -s $TUSIMPLEROOT data/tusimple +ln -s $DATASETROOT data/tusimple ``` For Tusimple, you should have structure like this: ``` -$TUSIMPLEROOT/clips # data folders -$TUSIMPLEROOT/lable_data_xxxx.json # label json file x4 -$TUSIMPLEROOT/test_label.json # test label json file +$DATASETROOT/clips # data folders +$DATASETROOT/lable_data_xxxx.json # label json file +$DATASETROOT/test_label.json # test label json file ``` ### LVLane -Download [LVLane](). Then extract them to `$TUSIMPLEROOT` just like TuSimple dataset. This link contains class annotations for TuSimple dataset too, replace the orginal ones with these. +Download [LVLane](). Then extract them to `$DATASETROOT` just like TuSimple dataset. This link contains class annotations for TuSimple dataset too, so replace the orginal labels ones with the new ones. +``` +$DATASETROOT/clips/0531/ +. +. +$DATASETROOT/clips/LVLane_train/ +$DATASETROOT/lable_data_xxxx.json +$DATASETROOT/test_label.json +$DATASETROOT/LVLane_test.json +$DATASETROOT/LVLane_train.json + +``` We need to generate segmentation from the json annotation. ```Shell -python tools/generate_seg_tusimple.py --root $TUSIMPLEROOT +python tools/generate_seg_tusimple.py --root $DATASETROOT # this will generate seg_label directory ``` ## Getting Started +If we want just detection, no lane classification, switch to `detection` branch by running `git checkout detection`. ### Training For training, run @@ -150,7 +162,7 @@ python tools/detect.py configs/resa/resa34_culane.py --img images\ ## Contributing -We appreciate all contributions to improve LaneDet. Any pull requests or issues are welcomed. +We appreciate all contributions to improve LVLane. Any pull requests or issues are welcomed. ## Licenses This project is released under the [Apache 2.0 license](LICNESE). From 57b21aca7001556a158e718eef2524d1d7a3ad57 Mon Sep 17 00:00:00 2001 From: Zillur Rahman <54721606+zillur-av@users.noreply.github.com> Date: Fri, 21 Jul 2023 17:43:15 -0700 Subject: [PATCH 5/5] Update README.md --- README.md | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 5d6e557..c93544c 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # LVLane ## Introduction -This repository is the official implementation of the paper "LVLane: Lane Detection and Classification in Challenging Conditions", accpeted in XYZ conference. +This repository is the official implementation of the paper "[LVLane: Lane Detection and Classification in Challenging Conditions](https://arxiv.org/abs/2307.06853)", accpeted in 2023 IEEE International Conference on Intelligent Trabsportation Systems (ITSC). ![demo image](.github/test-class-lvlane-ufld2.jpg) @@ -77,17 +77,17 @@ $DATASETROOT/test_label.json # test label json file ``` ### LVLane -Download [LVLane](). Then extract them to `$DATASETROOT` just like TuSimple dataset. This link contains class annotations for TuSimple dataset too, so replace the orginal labels ones with the new ones. +Download [LVLane](https://drive.google.com/file/d/1lRhne-d87A4b0gLjf6quipDQ4MYvP7ky/view?usp=sharing). Then extract them to `$DATASETROOT` just like TuSimple dataset. This link contains class annotations for TuSimple dataset, so replace the orginal labels ones with the new ones. Lane annotations and class labels of Caltech dataset are also available in TuSimple format. Download the dataset from original site and resize them to 1280x720 to use with this model. ``` $DATASETROOT/clips/0531/ . . -$DATASETROOT/clips/LVLane_train/ -$DATASETROOT/lable_data_xxxx.json +$DATASETROOT/clips/LVLane_train_sunny/ +$DATASETROOT/label_data_xxxx.json $DATASETROOT/test_label.json -$DATASETROOT/LVLane_test.json -$DATASETROOT/LVLane_train.json +$DATASETROOT/LVLane_test_sunny.json +$DATASETROOT/LVLane_train_sunny.json ``` @@ -176,14 +176,15 @@ This project is released under the [Apache 2.0 license](LICNESE). * [cfzd/Ultra-Fast-Lane-Detection](https://github.com/cfzd/Ultra-Fast-Lane-Detection) - + +```