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Our Project

Real-time implementation of Semantic Segmentation for traffic detection and autonomous driving applications.

Table of contents

About the Project

Aim of semantic segmentation is to accurately classify each pixel in an image into specific object or class categories, facilitating detailed scene understanding for various computer vision applications.

Semantic Segmentation.png

Inferences

Model Accuracy Validation Accuracy Epochs Dataset
UNet 27.39 22.36 5 ADE20KChallengeData2016
SegNet 39.80 30.17 10 ADE20KChallengeData2016
ERFNet 92.27 92.92 10 Aerial Imagery

Tech Stack

File Structure

📦 Semantic Segmentation 
 ┣ 📂Assets/Images                     # Contains images of the results and techniques
 ┣ 📂Dataset                           # Dataset 
 ┃ ┣ Dataset.md                        # Information about the dataset
 ┃ ┣ customize_dataset.py              # Use this while training it on custom datset
 ┣ 📂Implementation                     
 ┃ ┣ 📂Models                          # Information about models implemented from scratch
 ┃ ┃ ┣ 📂UNet
 ┃ ┃ ┣ UNet.md
 ┃ ┃ ┣ UNet.ipynb
 ┃ ┃ ┣ 📂ErfNet
 ┃ ┃ ┣ ErfNet.md
 ┃ ┃ ┣ ErfNet.ipynb
 ┃ ┃ ┣ 📂SegNet
 ┃ ┃ ┣ SegNetNet.md
 ┃ ┃ ┣ SegNet.ipynb
 ┃ ┃ ┣ 📂ShuffleNet                    #TheFinalarchitecture
 ┃ ┃ ┣ Architecture.md
 ┃ ┣ 📂Utils                            
 ┣ 📜README.md                         # About the project
 ┣ 📜project_report.docx               # Project Report
 ┗ 📜requirements.txt                  # Requirements

Getting Started

Prerequisites and installation

  • Download Python on your device if not already present. Refer here for the setup.
  • You can use any code editor.
  • All installations mentioned are made using pip hence install pip.
  • To install pip , follow this link
  • To install the requirements
pip install -r requirements.txt

Installation

  • Clone the repository
git clone https://github.com/extint/Semantic_Segmentation.git

Download the dataset

  • Download the ADE20K scene parsing dataset:
chmod +x download_ADE20K.sh
./download_ADE20K.sh

Theory and Approach

  • We aim to use the Convolutional Neural Network approch over the traditional algorithms because CNNs can learn features automatically, work end-to-end, maintain spatial information, offer adaptability and transfer learning, and achieve state-of-the-art performance and hence are more effective and versatile for this task. To know more about CNNs, go through this document

  • Overview of the architecture used: The architecture we've chosen for real-time semantic segmentation is MobileNetV2 because its efficiency is achieved through a combination of architectural innovations and design choices that reduce the model's computational complexity and memory footprint without sacrificing too much in terms of performance. It's an excellent choice for tasks like image classification, object detection, and more on mobile and embedded devices. To learn more about the architecture used, click here

Results and Demo

Original

Segmented Output

Future Work

  • Working on deep learning based computer vision technique of landmark detection.
  • Exploring other applications of semantic segmentation like medical, satellite imagery videos, night-vision, etc.
  • Implementation of MobileNet architecture completely from scratch
  • Pruning and Optimisation of model for deployment on OAK-D camera.

Contributors

Acknowledgements

Resources