Create README Image Segmentation for self-driving cars.md

ML Projects/Image Segmentation for self-driving cars/README.md

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+# Connect with me
+LinkedIn: https://www.linkedin.com/in/raj-goyal-b9a683230/
+Mail: [email protected]
+
+# Image-Segmentation-for-self-driving-cars 
+The rapid development of self-driving car technology has highlighted 
+the critical need for accurate and efficient image segmentation 
+systems. Image segmentation enables autonomous vehicles to 
+precisely identify and classify various elements in their environment, 
+such as pedestrians, vehicles, road signs, and lane markings. However, 
+existing segmentation algorithms often struggle with the complexity 
+and variability of real-world driving scenarios, leading to potential 
+safety risks.
+
+• Library required to build the model: Numpy, Pandas, Matplotlib, 
+Scikit-Learn, Scikit-Image
+
+• Framework Used: Tensorflow
+
+• Model: U-net CNN architecture.
+
+**Algorithm Selection**:
+
+• **Data Characteristics**:
+
+• High-resolution images with detailed annotations.
+
+• Diverse urban driving scenes with varied objects and environmental conditions.
+
+**• Why U-Net:**
+
+• Accuracy: High effectiveness in image segmentation, offering detailed segmentation maps.
+
+• Efficiency: Suitable for real-time applications due to computational efficiency.
+
+• Versatility: Handles variability in real-world scenarios, capturing fine-grained details and context.
+
+• Proven Success: Robust and reliable in complex segmentation tasks across different fields.
+
+**• Data Input:**
+
+• This dataset has 2975 training images files and 500 validation image files. Each image file is 256x512 pixels, and each file is a 
+composite with the original photo on the left half of the image, alongside the labeled image (output of semantic segmentation) on the 
+right half.
+
+**• Training Process:**
+
+• Used the data augmentation to create more data and used dropout layer additionally in the architecture. 
+
+**• Prediction Process:**
+
+• The trained algorithm takes the input images and it is passed through the virtual true mask by U-net model to predict the actual mask 
+segmented for the self-driving cars.
+
+**Importance**🔭
+
+• Importance of Image Segmentation for Self-Driving Cars
+
+• Obstacle Detection: Precise segmentation is crucial for identifying 
+obstacles (e.g., pedestrians, vehicles) to ensure safe navigation.
+
+• Traffic Sign Recognition: Accurate segmentation of traffic signs and signals 
+is essential for obeying traffic rules and ensuring safety.
+
+• Lane Marking Detection: Reliable segmentation of lane markings helps in 
+maintaining the correct lane and assists in lane-keeping systems.
+
+• Environmental Understanding: Comprehensive segmentation of the 
+driving environment (e.g., roads, sidewalks, buildings) improves the overall 
+situational awareness of autonomous vehicles.
+
+**References**
+• Dataset: https://www.kaggle.com/datasets/dansbecker/cityscapesimage-pairs/dat