Underwater Plastic Surveillance Data Labeler/Annotator
I utilized YOLOv9 to annotate and classify plastic waste within underwater images for an environmental monitoring project. The process involved designing and executing a detection pipeline that included stages of data acquisition, preprocessing, feature extraction, and post-processing to improve model accuracy. This hands-on work focused on ensuring annotation quality that would enhance the system's efficiency and precision in real-world aquatic scenarios. • Performed annotation and object detection on plastic waste in aquatic environments. • Ensured accurate bounding of objects for robust detection pipeline development. • Enhanced dataset with object-level annotations for improved YOLOv9 model training. • Achieved high precision and mAP scores through careful data labeling and testing.