A computer vision solution for traversable area segmentation

A computer vision solution for traversable area segmentation

Supervisor:   Dr Zhuoling Huang and Dr Geesara Kulathunga 

What you will be doing

Throughout this internship, you will explore the challenges of traversable area segmentation, gather and preprocess diverse datasets, and develop machine learning models, particularly using deep learning architectures. You’ll train and evaluate these models, optimizing them for efficiency on various hardware platforms. Your work will include creating a real-time segmentation pipeline and ensuring the system’s reliability through extensive testing and validation.

Figure 1: the expected environment

Figure 2: an example output (cited from Github https://github.com/chaytonmin/Off-Road-Freespace-Detection/tree/main)

What skills would be useful to have for this project 

  • Programming skills in Python or C++ 
  • Machine learning tools related to computer vision (e.g. Keras, TensorFlow, PyTorch) 
  • ROS2 would be helpful but it is not required.  

How to get more information and apply 

The prospective candidates can apply by sending an email to Geesara (gkulathunga@lincoln.ac.uk) or Zhuoling (zhuang@lincoln.ac.uk) and providing a short background summary focusing on relevant interests and skills together with a CV. All candidates meeting the skills requirements will be accept and in case of multiple expressions of interest, the project scope will be negotiated with individual candidates. The application deadline is 30th of June and the candidates will be informed about the selection outcome by the 4th of July with an anticipated project start on the 8th of July (this is negotiable).