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Detection and Segmentation of Kayakers using Mask R-CNN

This project is based on Mask R-CNN implementation.
The model generates bounding boxes and segmentation masks for each instance of a kayaker in the image.
Model is trained on custom dataset and it's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
The custom dataset consists of 65 kayaker images with pixel-by-pixel polygon annotations.

Input Output
input_video output_video

Performance measure of the model

Model Starting weights Training Layers Epochs Training mAP Test mAP
M1 COCO heads 5 0.844 0.800
M2 COCO heads 10 0.824 0.867
M3 COCO heads 15 0.844 0.867
M4 COCO heads 20 0.844 0.800
M5 COCO all 5 0.865 0.867
M6 COCO all 10 0.885 0.933
M7 COCO all 15 0.865 0.867
M8 COCO all 20 0.885 0.933

Getting Started

Prerequisites

  • Python 3.7

How To Run Project

  1. Clone this repository
 git clone https://github.com/ndakic/kayaker-detection
  1. Download yolov3 weights (237 MB) from here and add it to your model folder.
  2. Install the requirements using pip and venv
pip install -r requirements.txt
  1. Clone and install the Mask R-CNN Library
git clone https://github.com/matterport/Mask_RCNN

cd Mask_RCNN
python setup.py install
  1. Run the following command in the command line
 python application.py