Welcome to the Autonomous Waste Segregation Boat project, an innovative solution addressing water pollution by autonomously classifying and collecting waste in water bodies. Designed with deep learning and robotics, this boat navigates independently, identifying waste as biodegradable or non-biodegradable with a high level of accuracy. This work is a significant contribution to environmental tech, and it’s patented as a Waste Segregation System using Deep Learning.
Water pollution is one of today’s most pressing environmental issues. This project aims to create an effective, scalable, and real-time solution using a robotic boat to autonomously segregate waste floating in water bodies, enhancing waste management and contributing to cleaner ecosystems.
- Real-time Waste Detection: The boat employs TensorFlow and a ResNet deep learning model to perform real-time waste classification, distinguishing biodegradable from non-biodegradable waste.
- High Classification Accuracy: With an accuracy rate of 93.07%, the system sets a new standard in autonomous waste identification.
- Autonomous Navigation: Using Raspberry Pi for processing and navigation, the boat operates independently without human intervention.
- Patented Innovation: This project is protected under a published patent, establishing it as a novel approach in environmental waste management technology.
- Languages: Python
- Machine Learning Frameworks: TensorFlow, ResNet for image classification
- Computer Vision: OpenCV
- Hardware: Raspberry Pi, integrated camera system for real-time image capture and processing
- Robotic Components: Custom hardware setup with motors and sensors for navigation and waste collection
The project employs:
- Deep Learning: For accurate, real-time waste classification.
- Camera System: Captures continuous video feed for image processing.
- Raspberry Pi Control: Manages navigation and processing for autonomous operation.
- Raspberry Pi setup and basic Python environment.
- Hardware components for navigation (refer to hardware setup guide).
- Install necessary libraries with:
pip install -r requirements.txt