Deep Learning-Based Marine Debris Detection System

Project Title: Deep Learning-Based Marine Debris Detection System This project presents an intelligent system for detecting and classifying marine waste using Deep Learning techniques. Marine pollution is a critical global issue, and this solution aims to improve waste management through automation and accuracy. The system uses Convolutional Neural Networks (CNN) for feature extraction and Multi-Layer Perceptron (MLP) for classification. It is capable of identifying different types of waste such as plastic, metal, glass, paper, and cardboard from images. With an accuracy of over 92%, the model demonstrates reliable performance and can be applied in real-world scenarios like coastal monitoring and environmental protection. 🚀 Key Features Automated marine debris detection Image-based classification using deep learning High accuracy and efficiency Supports environmental sustainability 🧠 Technologies Used Python Deep Learning (CNN, MLP) Image Processing Machine Learning Libraries 🌍 Applications Coastal and ocean monitoring Smart waste management systems Environmental research Pollution control initiatives 🔮 Future Enhancements Real-time detection using cameras Drone-based monitoring systems Web or mobile application deployment Improved accuracy with larger datasets 📢 Conclusion This project highlights how artificial intelligence can be used to address environmental challenges and contribute to a cleaner and safer ecosystem.