车道线检测-基于lanenet实现的实时车道线检测-优质项目实战.zip

# LaneNet-Lane-Detection 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 0.9. 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:  The `Binary Segmentation loss` drops as follows:  The `Instance Segmentation loss` drops as follows:  ## Experiment The accuracy during training process rises as follows: