Zebra Crossing Detection

This is the model to predict the Zebra Cross in real time from driver's view.

Key features are :

• This model is trained with 1024*1024 images which is of real time camera quality.
• I used nuScenes deep drive dataset and also some custom images for training
• I took 2300 images out of 67k images from Nuscenes dataset and 200 real images from camera
• Here I used Tensorflow 2 object detection which is the latest object detection library of google
• I used SSD Resnet 50 1024*1024 model for training purpose
• I got DetectionBoxes_Precision/[email protected] nearly 82% which is good for 2D image detection in road.

Steps to test the model:

1.Clone this repository by downloading zip or by running following command in Git or terminal :

git clone https://github.com/yshastri66/Zebra_cross_detection.git

2.Create a new python environment and install the requirments using requirment.txt file by executing following commands :

conda create -n env_name
conda activate env_name
pip install -r requirments.txt

3.Put all your images which you want to detect in 'test' folder.

4.Run the jupyter notebook image_testing.ipynb

5. comment out following lines if you are not using GPU :

physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0],True)

6. All your images with predictions will be saved in predictions folder.

Sample predicted images are given in the folder sample_predictions. Look into it for better understanding.

If you are having any douts, feel free to contact me below:

Yashodhara Shastri G

Contact : [email protected]