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Deep Learning Approaches for Ocean Waste Detection: A Comparative Study of YOLOv8 and Faster R-CNN
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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.
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Researchers developed an improved Faster R-CNN deep learning model for identifying and detecting microplastic particles in marine environments. The model achieved an average detection confidence of 99% and successfully distinguished polystyrene microplastics from mixed particle suspensions across varying backgrounds and conditions, demonstrating a promising automated approach for monitoring microplastic pollution.
Deep Learning-Based Underwater Trash Detection System UsingYOLOv8
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Researchers trained a YOLOv8 deep learning model on underwater debris imagery to enable real-time detection and classification of submerged plastic trash, demonstrating substantially faster and more scalable performance than traditional manual survey or sonar-based methods.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
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This review examines deep learning approaches for automated underwater trash detection, covering CNN-based architectures including YOLO and Faster R-CNN, and finds they outperform traditional sonar and manual inspection methods while identifying key challenges such as low visibility and limited labeled datasets.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
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This review examines deep learning approaches for automated underwater trash detection, covering CNN-based architectures including YOLO and Faster R-CNN, and finds they outperform traditional sonar and manual inspection methods while identifying key challenges such as low visibility and limited labeled datasets.
AI – Driven Marine Debris Detection for Ocean Conservation
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Researchers developed an AI-driven marine debris detection system using the YOLOv8 deep learning model trained to identify plastic waste in challenging underwater conditions including low visibility and complex backgrounds. The system aims to provide scalable, automated monitoring to support ocean conservation and guide debris removal efforts.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.