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Unofficial implemention of lanenet model for real time lane detection
| Date | Stars |
|---|---|
| 2026-07-24 | 2564 |
| 2026-07-25 | 2564 |
| 2026-07-28 | 2564 |
| 2026-07-30 | 2564 |
| 2026-08-06 | 2564 |
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# 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`  ## 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](http://benchmark.tusimple.ai/#/). 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. The trained lanenet model weights files are stored in [lanenet_pretrained_model](https://www.dropbox.com/sh/0b6r0ljqi76kyg9/AADedYWO3bnx4PhK1BmbJkJKa?dl=0). You can download the model and put them in folder weights/tusimple_lanenet/ You may also download the pretrained model via [BaiduNetDisk here](https://pan.baidu.com/s/1sLLSE1CWksKNxmRIGaQn_A) and extract code is `86sd`. You can test a single image on the trained model as follows ``` python tools/test_lanenet.py --weights_path /PATH/TO/YOUR/CKPT_FILE_PATH --image_path ./data/tusimple_test_image/0.jpg ``` The results are as follows: `Test Input Image`  `Test Lane Mask Image`  `Test Lane Binary Segmentation Image`  `Test Lane Instance Segmentation Image`  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/YOUR/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. `Tusimple test dataset gif`  ## 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 consist of three components, a binary segmentation label file, a instance segmentation label file and the original image. The binary segmentation uses 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.
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Kunal Goyal · India
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:40f329a2b957ddbe, topic:deep-learning, topic:tensorflow
matched fp:40f329a2b957ddbe, topic:self-driving-car