A CNN-based solution using the Airbus Ship Detection dataset that processes satellite imagery for ship detection, achieving 88.54% accuracy.

Academic project (19CSE305 Machine Learning course) focused on:

• 🛥️ Ship detection in satellite imagery
• 🔍 Feature extraction techniques
• 🧠 CNN architecture implementation
• 📊 Binary image classification

• 📊 Mean pixel value extraction from RGB layers
• ⚖️ Otsu threshold masking
• 🔄 Hu Moments for shape characterization
• 🧮 CNN with BatchNormalization
• 🎯 88.54% accuracy achievement

• Python
• TensorFlow/Keras
• OpenCV
• NumPy
• Pandas
• Matplotlib

• Input shape: 256x256x3 (RGB)
• Convolutional layers with 32 filters
• ReLU activation
• MaxPooling with 2x2 pool size
• 25% dropout rate
• Batch normalization

1. Mean Pixel Value

• Reduces 3 RGB layers to 1 layer
• Calculates mean of R, G, B values per pixel

2. Otsu Threshold Masking

• Calculates threshold per image
• Binary output (0, 255)

3. Hu Moments

• Shape characterization
• Ship feature extraction

• [1] Analytics Vidhya - Feature Extraction Techniques
• [2] Otsu's Method - Wikipedia
• [3] Hu Moments - CV Explained