REPOSITORY STRUCTURE:
- Data Loader Program file - Used to load and preprocess data from IDD dataset, and save preprocessed data as NUMPY files in 'Prepared data' Folder (Download dataset from THIS LINK). Access 'Prepared Data' Folder from my Google drive link
- SegNet Architecture - This program is used to define the SegNet architecture model and save it as .H5 file
- Model Trainer - This program import train data (numpy), validation data (numpy) and the SegNet Model to train the same, and save trained model into new .H5 file
- Model Tester - Imports trained model as well as testing data, and the model is evaluated by using various metrics and ROC curve
Motivation
The motivation behind the development of a road detection system tailored for unstructured Indian roads stems from the critical need to address the unique challenges posed by the country's diverse and often chaotic road networks. With millions of kilometers of roads traversing varied terrain, densely populated urban areas, and remote rural regions, India's transportation infrastructure presents a complex environment characterized by irregularities, inadequate signage, and unpredictable traffic patterns. Traditional road detection methods struggle to cope with these challenges, leading to safety hazards, traffic congestion, and inefficiencies in transportation systems. By leveraging advanced deep learning techniques and specifically tailored architectures such as SegNet, there exists an opportunity to revolutionize road detection capabilities, enhancing road safety, navigation accuracy, and overall transportation efficiency across the country. Moreover, the potential societal impact of a robust road detection system extends beyond mere convenience, encompassing improved emergency response, reduced accident rates, and enhanced accessibility for citizens across all demographics. Thus, the motivation behind this project lies in leveraging cutting-edge technology to address a pressing societal need, ultimately contributing to the advancement of road infrastructure and safety standards in India.
Overview
The project aims to develop a robust road detection system specifically designed for unstructured Indian roads using the SegNet architecture, a convolutional neural network (CNN) optimized for semantic segmentation tasks. Unstructured Indian roads pose unique challenges, including irregular road layouts, diverse terrain, varying lighting conditions, and unpredictable traffic patterns. Traditional road detection methods often struggle to accurately delineate road regions in such environments, leading to safety hazards and transportation inefficiencies.
To address these challenges, the project will focus on several key components:
Data Collection and Annotation: A comprehensive dataset of diverse road scenes from different regions across India will be collected and annotated. This dataset will serve as the foundation for training and evaluating the SegNet model, ensuring its adaptability to the wide range of environmental conditions and road types present in the Indian context.
Model Development and Optimization: The SegNet architecture will be implemented and trained using the annotated dataset. Various optimization techniques, including transfer learning and data augmentation, will be employed to enhance the model's performance and generalization capabilities. Fine-tuning of model parameters will be crucial for achieving high accuracy and robustness in road detection.
Evaluation Metrics: Comprehensive evaluation metrics, including F1 score, accuracy, precision, and recall, will be developed and implemented to quantitatively assess the performance of the road detection system. These metrics will provide insights into the system's overall effectiveness in correctly identifying road regions while minimizing false positives and false negatives.
Real-world Deployment Considerations: Practical considerations for deploying the road detection system in real-world settings will be investigated, including computational efficiency, hardware requirements, and integration with existing transportation infrastructure. The system's scalability, adaptability, and compatibility with diverse vehicle platforms will be evaluated to ensure its viability for widespread adoption and deployment across Indian road networks.
By addressing these components, the project aims to contribute to the development of an advanced road detection system tailored to the unique challenges of Indian road environments. Ultimately, the system will enhance road safety, navigation accuracy, and transportation efficiency, thereby improving the overall quality of life for citizens across the country.
FUNCTIONAL REQUIREMENTS
Functional requirements define the specific functions, features, or capabilities that a software system must possess to satisfy user needs and achieve its intended purpose. In the context of your road detection system for unstructured Indian roads using the SegNet architecture, the functional requirements can be outlined as follows:
Image Loading and Preprocessing:
The system should be able to load input images of road scenes from various sources, such as local storage or online repositories. It should preprocess the input images to ensure uniformity in size, resolution, and format, facilitating consistent model input. Annotation Handling:
The system should handle ground truth annotations associated with the input images, typically in the form of binary masks indicating road regions. It should provide functionality to load, process, and manipulate annotation data for training and evaluation purposes. Model Training:
The system should support the training of the binary SegNet model using the annotated training dataset. It should facilitate the configuration of training parameters such as batch size, number of epochs, learning rate, and optimizer choice. It should enable the monitoring of training progress, including metrics such as loss and accuracy. Model Inference:
The system should allow the trained model to perform inference on new, unseen road images to predict road regions. It should support batch inference for efficient processing of multiple images. Thresholding and Post-processing:
The system should include functionality to apply thresholding techniques to the model predictions, converting probability maps to binary masks. It should provide options to adjust threshold values based on user preferences or performance requirements. Post-processing techniques such as morphological operations (e.g., erosion, dilation) may also be applied to refine the predicted masks. Evaluation and Performance Metrics:
The system should evaluate the performance of the model predictions using various metrics, including F1 score, precision, recall, accuracy, and ROC curves. It should generate performance reports summarizing the model's effectiveness in road detection tasks. Visualization and Result Interpretation:
The system should offer visualization tools to display input images, ground truth annotations, predicted masks, and overlay visualizations. It should provide interactive interfaces for users to interpret and analyze the results, facilitating qualitative assessment of the model's performance. Model Persistence and Versioning:
The system should support the saving and loading of trained model checkpoints to ensure model persistence across sessions. It should enable versioning of models and experiments to track changes and improvements over time. Scalability and Performance Optimization:
The system should be scalable to handle large datasets and accommodate future expansion in terms of data volume and model complexity. It should incorporate performance optimization techniques to minimize computational resources and inference time, ensuring efficient operation on different hardware platforms. Integration and Deployment:
The system should integrate seamlessly with existing software environments and frameworks commonly used in machine learning and computer vision workflows. It should provide deployment options for deploying trained models into production environments, including cloud platforms or edge devices.
TOOLS USED
In the implementation of the road detection system based on the SegNet architecture, several tools and libraries are utilized to facilitate different stages of the development process. Here are the primary tools used:
Python Programming Language: Python serves as the primary programming language for implementing the road detection system due to its simplicity, versatility, and extensive support for deep learning frameworks and libraries.
TensorFlow and Keras: TensorFlow and Keras are deep learning frameworks used for developing and training neural network models. TensorFlow provides low-level APIs for building custom models and optimization algorithms, while Keras offers a high-level API for rapid prototyping and experimentation.
OpenCV: OpenCV (Open Source Computer Vision Library) is a popular open-source computer vision and image processing library. It is utilized for various tasks in the road detection system, such as image loading, preprocessing, and post-processing operations.
NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for multidimensional arrays and mathematical functions, making it essential for handling and processing image data efficiently.
Matplotlib: Matplotlib is a plotting library for creating static, animated, and interactive visualizations in Python. It is used to generate graphical representations of images, binary masks, evaluation metrics, and other analysis results.
Pillow: Pillow is a Python Imaging Library (PIL) fork used for image processing tasks such as opening, manipulating, and saving various image file formats. It is employed for loading and saving road images in different preprocessing and visualization steps.
Google Colab: Google Colab is a cloud-based platform provided by Google for running Python code in a Jupyter notebook environment. It offers free access to GPU and TPU resources, making it suitable for training deep learning models with large datasets.
By leveraging these tools and libraries, developers can efficiently implement, train, evaluate, and deploy the road detection system based on the SegNet architecture, effectively detecting road pixels in unstructured Indian road scenes.