Rebeca Villarraso

Computer Vision Testing Analyst

1 minute read

Objectives

  1. Contact to Goose dataset developers to find out data availability. Study dataset structure.
  2. Prepare the work environment in Github.
  3. Study other data sets (RUGD, FIRE, CITYSCAPES).
  4. Obtain RELLIS-3D dataset and know its structure.

  5. Develop semantic segmentation algorithm for testing.

1. Goose Dataset

This week I was getting to know the Goose dataset website, getting to know its data structure, reading related works and getting to know the neural network models used for semantic segmentation.

2. Contact to Goose developers

I contacted the developers of the Goose dataset to congratulate them and request when 2D images for semantic segmentation and LIDAR data will be available. They responded very kindly to me that within a couple of weeks the 2D images would be available and the rest of the data would be available at the end of May/June.

3. Work environtment on Github

Added different sections in Github to save information from different datasets that can be useful. Added documentation and a tutorial with the necessary steps to work with “SuperGradients” on Goose.

4. Study of other datasets

The following data sets were downloaded to the local computer:

  • RUGD
  • FIRE
  • CITYSCAPES
  • RELLIS 3D
  • INSTANCE-LEVEL_HUMAN_PARSING

5. Semantic Segmentation

  • DeepLabV3+ semantic segmentation on “instance-level_human_parsing” dataset. (DeepLabV3+ is a ResNet50 pretrained model variation based on enconder-decoder blocks). See: https://github.com/RoboticsLabURJC/2024-tfm-rebeca-villaraso/tree/main/docs/TESTS/Test1_DeepLabV3%2B for more details.
  • RELLIS-3D (a Multi-modal Dataset for Off-Road Robotics) (images and LIDAR) obtained and preprocessed from: https://gamma.umd.edu/publication.

  • Reproduction of Ga-Nav Sematic Segmentation of Rellis-3D Images according to: https://github.com/unmannedlab/RELLIS-3D

  • On going: adaptation the DeepLabV3+ model to train RELLIS-3D dataset.

Next Week Work Planning

  1. Continue adapting DeepLabV3+ with the RELLIS-3D and RUGD datasets.
  2. Perform Semantic Segmentation on Cityscapes dataset.
  3. GitHub Pages.
  4. Study the metrics and models proposed on the Cityscapes dataset website: https://www.cityscapes-dataset.com/benchmarks/

References

  • [RUGD Dataset] http://rugd.vision/
  • [FIRE Dataset] https://universe.roboflow.com/fire-dataset-tp9jt/fire-detection-sejra
  • [CITYSCAPES Dataset] https://www.cityscapes-dataset.com/
  • [INSTANCE-LEVEL_HUMAN_PARSING Dataset] https://paperswithcode.com/paper/instance-level-human-parsing-via-part
  • [RELLIS-3D Dataset] https://github.com/unmannedlab/RELLIS-3D