Duplicate from yaraa11/road-lane-semantic-segmentation-unet-resnet50

Co-authored-by: Yara Elshehawi <yaraa11@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+datasets:
+- yaraa11/enhanced-road-segmentation-dataset
+language:
+- en
+pipeline_tag: image-segmentation
+tags:
+- adas
+- autonomous-driving
+- unet
+- lane-detection
+- semantic-segmentaion
+- computer-vision
+---
+
+
+# 🛣️ 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.
+
+---
+
+## 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  
+
+---
+
+## 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
+
+---
+
+## Repository
+
+Full training, fine-tuning, and inference pipelines are available [here](https://github.com/yaraeslamm/lane-detection)
+
+---
+
+## 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.
+
+---
+
+## 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.
+
+---
+
+## Class Mapping
+
+| Class ID | Label        |
+|--------:|-------------|
+| 0 | Background |
+| 1 | Road |
+| 2 | Lane Markings |
+
+---
+
+## 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 |
+
+---
+
+## 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)
+
+```python
+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](https://github.com/yaraeslamm/lane-detection)
+
+
+---
+
+## 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
+
+---
+
+## 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.
+
+---
+
+## License
+
+This model is released under the **Apache 2.0 License**, allowing commercial and research use with attribution.
+
+---
+
+## Author
+
+Developed by Yara Elshehawi  
+Check out my other work:
+[Portfolio](https://yaraeslamm.github.io)
+[LinkedIn](https://www.linkedin.com/in/yara-eslam-877421212/)

road_segmentation.keras

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