🚢 Airbus Ship Detection - ML Course Project

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

🎯 Project Overview

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

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

🔑 Key Features

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

🛠️ Technical Stack

Python
TensorFlow/Keras
OpenCV
NumPy
Pandas
Matplotlib

🏗️ Model Architecture

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

📈 Feature Extraction Process

Mean Pixel Value

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

Otsu Threshold Masking

Calculates threshold per image
Binary output (0, 255)

Hu Moments

Shape characterization
Ship feature extraction

📚 References

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