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Deep Learning Approaches for Ocean Waste Detection: A Comparative Study of YOLOv8 and Faster R-CNN
Summary
Deep learning models (YOLOv8 and Faster R-CNN) were compared for detecting ocean waste, with Faster R-CNN achieving better precision for meso- and macroplastic detection, though both systems struggled with microplastics due to their tiny size and visual similarity to natural marine particles. This highlights the urgent need for specialized AI detection methods capable of identifying microplastics in ocean monitoring applications.
Ocean plastic pollution has reached critical levels because it damages marine ecosystems and biodiversity while creating health risks for humans. The current methods for marine debris detection and monitoring face multiple obstacles because they lack scalability and operate inefficiently while processing data at a slow rate. The EcoVision project introduces an AI solution which employs machine learning and computer vision to perform automatic ocean waste detection. The system achieves better results through Faster R-CNN integration which enhances both precision and object detection accuracy in complex marine environments. The system follows a structured process that includes data collection and preparation followed by model development and assessment using performance indicators including accuracy and precision and recall and F1-score. The experimental findings show that the system achieves better results in detecting meso- and macroplastics yet it struggles to identify microplastics because of their tiny size and resemblance to ocean natural elements. The EcoVision system demonstrates how deep learning technology can solve real-world environmental problems while supporting environmental policy development and specific pollution reduction efforts in ocean waters.