Concept Detection of Plastic, Oil, Micro Algae

The detection and monitoring of environmental pollutants, such as plastics, oil spills, and microalgae, in water bodies are critical for preserving marine ecosystems. This study focuses on leveraging computer vision and machine learning algorithms to identify and classify these pollutants in oceanic environments. Using YOLO V9 (You Only Look Once) model, we perform object detection on three key datasets: ocean plastics waste detection, oil spill detection, and microalgae detection. The first dataset is utilized to detect various plastic pollutants, including beverage cans, glass bottles, plastic bags, and other waste materials. The second dataset is used to identify and classify oil spills, distinguishing between no oil spill and different types of oil spillage. Lastly, the third dataset is focused on detecting microalgae, an important parameter for assessing ocean health. YOLO V9, a state-of-the-art deep learning model known for its efficiency in real-time object detection, is employed for all three tasks. This approach contributes to better environmental monitoring and supports the efforts toward preserving marine biodiversity.

Keywords: Environmental monitoring, Object detection, YOLO V9, Ocean plastics, Oil spill detection, Microalgae detection, Marine ecosystems, Deep learning