Sea Trash Detection And Cleanup System
Budget: ₹100 – ₹400 INR
The Sea Trash Detection and Cleanup System is an AI-powered solution designed to identify and assist in the removal of marine debris from underwater environments. The system leverages deep learning and computer vision techniques to detect and classify different types of sea trash such as plastic, metal, fabric, rubber, fishing gear, and wood from underwater images and video streams.
A convolutional neural network (CNN) model is trained on a labeled dataset of marine objects, including both trash and marine life, enabling accurate differentiation between debris and natural underwater elements. Automated data preprocessing and image augmentation techniques are used to improve model performance and robustness under varying underwater conditions such as low visibility, lighting changes, and water turbidity.
Once trash is detected, the system highlights its location and category, providing actionable information that can be used by autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), or human divers for targeted cleanup operations. This reduces manual inspection effort and improves the efficiency of marine cleanup missions.
The project aims to support ocean conservation by enabling faster identification of polluted areas and assisting environmental agencies in planning and executing cleanup strategies. By combining artificial intelligence with marine robotics and imaging technology, the system contributes toward sustainable ocean management and protection of marine ecosystems.
A convolutional neural network (CNN) model is trained on a labeled dataset of marine objects, including both trash and marine life, enabling accurate differentiation between debris and natural underwater elements. Automated data preprocessing and image augmentation techniques are used to improve model performance and robustness under varying underwater conditions such as low visibility, lighting changes, and water turbidity.
Once trash is detected, the system highlights its location and category, providing actionable information that can be used by autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), or human divers for targeted cleanup operations. This reduces manual inspection effort and improves the efficiency of marine cleanup missions.
The project aims to support ocean conservation by enabling faster identification of polluted areas and assisting environmental agencies in planning and executing cleanup strategies. By combining artificial intelligence with marine robotics and imaging technology, the system contributes toward sustainable ocean management and protection of marine ecosystems.