Segmentation of Indian Traffic

This project explores semantic segmentation of Indian traffic scenes using deep learning. It implements and compares two models: a classic UNet and a custom-built CANet architecture. The goal is to segment images from the India Driving Dataset to understand real-world unstructured traffic environments.

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📌 Project Highlights

• Transfer Learning of UNet and a custom implementation of CANet for semantic segmentation
• Data preprocessing, augmentation, and label remapping specific to the India Driving Dataset
• Evaluation based on Intersection over Union (IoU) metric
• End-to-end pipeline including training, evaluation, and inference

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📂 Dataset

This project uses the India Driving Dataset (IDD) from idd.insaan.iiit.ac.in. The dataset contains images captured from a front-facing car camera driven around Hyderabad, Bangalore, and outskirts, along with polygon annotations in JSON format.

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🧱 Model Architecture

• UNet: A widely-used encoder-decoder CNN for image segmentation, leveraging skip connections for precise localization.
• Custom CANet: An attention-based convolutional architecture designed to improve feature representation. Further improvements planned to address class imbalance and optimize performance.

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📈 Evaluation & Results

Observations:

• UNet achieved an IoU score of 49.99%, outperforming CANet, which achieved 33.56%.
• CANet's lower performance is influenced by class imbalance within the dataset.
• Future improvements for CANet include applying class-weighted loss functions, focal + dice loss, or data sampling strategies to mitigate class imbalance.
• Additional experiments with architecture depth, attention mechanisms, and data augmentation could enhance results.

Conclusion:

• UNet currently provides stronger segmentation results on the IDD dataset.
• Addressing class imbalance and refining CANet architecture are key next steps.
• These improvements aim to enhance CANet’s effectiveness in handling real-world traffic scene segmentation.

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🔍 Sample Output

these results on the same images

UNet Segmentation Output

CANet Segmentation Output

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🛠️ Development Environment

🧪 Virtual Environment

• Key Packages:
  • Python 3.10
  • tensorflow==2.19.0
  • tf-keras==2.19.0
  • keras==3.9.2
  • pandas==1.4.2
  • numpy==1.26.4
  • matplotlib==3.10.0
  • pillow==11.2.1
  • albumentations==2.0.6
  • imgaug=0.4.0
  • segmentation-models==1.0.1