The project addresses the issue of growing underwater waste in oceans and seas. It offers three solutions: YoloV8 Algorithm-based underwater waste detection, a rule-based classifier for aquatic life habitat assessment, and a Machine Learning model for water classification as fit for use or not fit. The first model was trained on a dataset of 5000 images, while the second model used chemical properties guidelines from US EPA and WHO. The third model was trained on a dataset with over 6 million rows, providing reliable water classification results.
Underwater Waste Detection Using YoloV8
- Can detect underwater waste based on input images.
- Classifies water as potable or not based on chemical properties of water
- Classifies water as habitual for aquatic life or not.
- Python
- Dark Channel Prior Algorithm
- YoloV8(from Ultralytics)
- Xgboost-Classifier
Input Images:
Denoised Images After Running Dark Channel Prior Algorithm:
Output Images:
