Cleansea Project

Mask R-CNN for Underwater Debris Detection and Segmentation

This project uses the Mask R-CNN algorithm to detect underwater debris. The goal is to train the Mask R-CNN neural network algorithm using our hand-labeled dataset of underwater debris in order to achieve a successful detection of segmentation of debris under the sea. This imlementation relies on the Mask R-CNN Repository by Matterport, Inc. (see here).

The main purpose of this research consists:

New Dataset generation: Since most existing datasets for object detection are not applicable in this type of task, a dataset of underwater captured debris has been labeled with up to 19 different labels.
Trainning and fine-tunning Mask R-CNN model: Once we have the data needed for this task, a fine-tunning process is needed for achieving good results-
Image, Video and Real Time Detection: As an evaluation phase, we test our trained model analicing different images, videos and real time detections using a simulated enviroment.

Cleansea Dataset Image examples

Hand-labeled dataset of underwater debris is shown below:

In order to download the images related with the annotation files of this dataset visit the download link

Cleansea Trained Model detection and segmentation examples

Ground Truth	Prediction

Citation

Use this bibtex to cite this repository:

@inproceedings{asferrer2022,
      author    = {Alejandro Sanchez Ferrer, Antonio Javier Gallego, Jose Javier Valero-Mas, and Jorge Calvo-Zaragoza},
      title     = {Underwater Debris Detection using Regression Neural Networks},
      booktitle = {Iberian Conference on Pattern Recognition and Image Analysis},
      year      = {2022}
    }

Requirements

Python 3.7.11, TensorFlow 2.4.1, Keras 2.4.3 and other common packages listed in requirements.txt.

Installation

Follow the installation/README.md file for further instructions on installing dependencies on a conda enviroment.

(Optional) To train or test on MS COCO install pycocotools from one of these repos. They are forks of the original pycocotools with fixes for Python3 and Windows (the official repo doesn't seem to be active anymore).

* Linux: https://github.com/waleedka/coco
* Windows: https://github.com/philferriere/cocoapi.
You must have the Visual C++ 2015 build tools on your path (see the repo for additional details)

Example of usage