dlckdtjq153/road-lane-semantic-segmentation-unet-resnet50

🛣️ Road & Lane Semantic Segmentation (U-Net with ResNet-50 Encoder)

Model Description

This model performs multi-class semantic segmentation for driving scenes, focusing on:

• Background
• Drivable road area
• Lane markings

It is designed as a perception module similar to those used in ADAS and autonomous driving pipelines, where structured lane visualization is derived directly from segmentation outputs (no classical lane detection or Hough transform is used).

The model follows a U-Net–style encoder–decoder architecture with a ResNet-50 backbone pre-trained for feature extraction.

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Architecture

• Encoder: ResNet-50 (pre-trained)
• Decoder: U-Net–style upsampling path with skip connections
• Output: Softmax over 3 classes
  • 0: Background
  • 1: Road
  • 2: Lane markings

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Intended Use

This model can be used for:

• Academic research in road scene understanding
• ADAS perception experiments
• Lane visualization systems based on segmentation
• Educational projects in computer vision and deep learning

Limitations

• Not intended for real-world autonomous driving deployment
• Performance may degrade under:
  • Night conditions
  • Heavy rain or fog
  • Unusual camera angles
• Fine-tuning is recommended for different countries, road types, or camera setups

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Repository

Full training, fine-tuning, and inference pipelines are available here

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Training Data

The model was trained on a custom enhanced dataset, originally based on:

• Semantic Segmentation Makassar (IDN) Road Dataset
  (~374 labeled images)

Dataset Enhancements

To improve generalization, the dataset was expanded using:

• Random rotations
• Horizontal flipping
• Brightness and contrast jitter

Final dataset size: ~1,496 images

The dataset is publicly available and not included in this repository due to size constraints.

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Training Procedure

• Loss Function: Sparse Categorical Crossentropy
• Optimizer: Adam
• Learning Rate: 1e-4
• Epochs: 20
• Metrics:
  • Sparse Categorical Accuracy
  • Mean IoU
  • Lane-class IoU

Multiple experiments were conducted with different augmentation strategies and training schedules.
Final model selection prioritized lane IoU stability and visual consistency, not only numerical metrics.

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Class Mapping

Class ID	Label
0	Background
1	Road
2	Lane Markings

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Evaluation Results

Metric	Training	Validation
Accuracy	0.9972	0.9954
Mean IoU	0.9456	0.9401
Lane IoU	0.8517	0.8542
Loss	0.0069	0.0146

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How to Use

This model is intended to be used with custom inference pipelines.

Typical inference steps:

1. Resize input image to model input size
2. Normalize pixel values
3. Run forward pass
4. Apply argmax over softmax output to get class IDs
5. Visualize lane pixels or overlay segmentation mask

Example (TensorFlow / Keras)

import tensorflow as tf
import cv2
import numpy as np

# -------- Load model --------
model = tf.keras.models.load_model(
    "model_path",
    compile=False
)

# -------- Load & preprocess image --------
img_path = "image_path"

orig = cv2.imread(img_path)
orig = cv2.cvtColor(orig, cv2.COLOR_BGR2RGB)

img = cv2.resize(orig, (256, 256))
img_norm = img / 255.0
img_input = np.expand_dims(img_norm, axis=0)

# -------- Predict --------
pred = model.predict(img_input)
mask = np.argmax(pred[0], axis=-1)  # (256, 256)

# -------- Create color mask --------
# Class mapping:
# 0 = background, 1 = road, 2 = lane

color_mask = np.zeros((256, 256, 3), dtype=np.uint8)

color_mask[mask == 1] = (255, 0, 0)   # Road -> Red
color_mask[mask == 2] = (0, 255, 0)   # Lane -> Green

# -------- Overlay full segmentation --------
overlay_full = cv2.addWeighted(img.astype(np.uint8), 0.6, color_mask, 0.4, 0)

# -------- Lane-only overlay --------
lane_mask = np.zeros_like(color_mask)
lane_mask[mask == 2] = (0, 255, 0)

overlay_lane = cv2.addWeighted(img.astype(np.uint8), 0.7, lane_mask, 0.3, 0)

# -------- Show results --------
cv2.imshow("Original", cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2BGR))
cv2.imshow("Segmentation Overlay (Road + Lane)", cv2.cvtColor(overlay_full, cv2.COLOR_RGB2BGR))
cv2.imshow("Lane Only Overlay", cv2.cvtColor(overlay_lane, cv2.COLOR_RGB2BGR))

cv2.waitKey(0)
cv2.destroyAllWindows()

For full pipelines including video processing and lane visualization,
see the GitHub repository

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Fine-Tuning

Fine-tuning is recommended if:

• Using different road environments
• Working with different camera perspectives
• Wanting to rebalance lane vs road classes

Training scripts support:

• Freezing the encoder
• Full model retraining
• Custom datasets with RGB masks converted to class IDs

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Ethical Considerations

This model is intended for research and educational purposes only.

It should not be used as a sole perception system in safety-critical or real-world autonomous driving applications.

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License

This model is released under the Apache 2.0 License, allowing commercial and research use with attribution.

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Author

Developed by Yara Elshehawi
Check out my other work: Portfolio LinkedIn