parthkvv / Lane-Detection-in-Unstructured-Environments

A novel recurrent neural network for lane detection based on LSTM. Achieved 2% improvement in accuracy and 150% reduction in inference cost on Indian Driving Dataset (IDD) over CRF-based methods

Dataset Preparation

set the path till "dataset\train_set" in config.py file

"dataset\train_set" directory should consist of following files:
	"train_img" - Folder containing all the ground truth images
	"train_seg_img" - Folder containing all the binary label images

	"val_img" - Folder containing all the ground truth images
	"val_seg_img" - Folder containing all the binary label images

txt_file_gen_SCNN.py
	- generate txt files from image dataset, saved in dataset/train_set/seg_label/list
	  "train_gt.txt"
	  "val_gt.txt"
	  "test_gt.txt"	

CULane format data (Optional)

CULane
├── driver_100_30frame
├── driver_161_90frame
├── driver_182_30frame
├── driver_193_90frame
├── driver_23_30frame
├── driver_37_30frame
├── laneseg_label_w16
├── laneseg_label_w16_test
└── list

Note: Enter absolute path.

Tusimple format data (Optional)

Tusimple
├── clips
├── label_data_0313.json
├── label_data_0531.json
├── label_data_0601.json
└── test_label.json

Trained Model

Model trained on CULane Dataset can be converted from official implementation， which can be downloaded here. Please put the vgg_SCNN_DULR_w9.t7 file into experiments/vgg_SCNN_DULR_w9.

python experiments/vgg_SCNN_DULR_w9/t7_to_pt.py

Model will be cached into experiments/vgg_SCNN_DULR_w9/vgg_SCNN_DULR_w9.pth.

For single image demo test:

python demo_test.py   -i demo/demo.jpg 
                      -w experiments/vgg_SCNN_DULR_w9/vgg_SCNN_DULR_w9.pth 
                      [--visualize / -v]

Train

1. Specify an experiment directory, e.g. experiments/exp0.

2. Modify the hyperparameters in experiments/exp0/cfg.json.

   saved models

   • \experiments\exp0\

   Number of epochs

   • \experiments\exp0\cfg.json "MAX_EPOCHES": 60

3. Start training:

    - tools/train.py --exp_dir ./experiments/exp0 [--resume/-r]

4. Monitor on tensorboard:

   tensorboard --logdir='experiments/exp0'

Test

python demo_test.py -i E:\Abhishek\Lane_Detection\CULane\parth\SCNN\SCNN_Pytorch-master\demo\demo.jpg -w E:\Abhishek\Lane_Detection\CULane\parth\SCNN\SCNN_Pytorch-master\experiments\exp0\exp0_best.pth

• EVALUATE ON CUSTOM TEST DATASET : test_tusimple.py --exp_dir ./experiments/exp0 (keep one category of test data at a time)

(Optional) For Tusimple

python test_tusimple.py --exp_dir ./experiments/exp0

Evaluation

- \dataset\Evaluate\
  	- Generate csv files with results

Demo