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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.
Plastic pollution in aquatic environments poses a significant threat to marine ecosystems, biodiversity, and human health. Traditional methods for underwater waste detection, such as manual surveys and sonar imaging, are inefficient, time-consuming, and lack scalability. This paper presents a deep learning-based system for real-time detection and classification of underwater trash using the YOLOv8 model. The proposed system is trained on a dataset comprising underwater debris images, including plastic bottles, fishing nets, glass materials, electronic waste, and other pollutants. The model is integrated into a web-based application developed using Django, enabling users to upload images and videos for detection. The system processes inputs frame-by-frame and highlights detected objects with bounding boxes and classification labels. Experimental results demonstrate that the proposed system achieves high accuracy and real-time performance even in challenging underwater conditions such as low visibility and object distortion. The solution provides an efficient and scalable approach for environmental monitoring and supports marine conservation efforts.
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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.
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.
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Researchers developed a deep learning model using projector-based feature extraction to classify underwater garbage images, achieving high accuracy in identifying marine plastic debris and other waste types for automated ocean pollution monitoring.
Underwater Waste Recognition and Localization Based on Improved YOLOv5
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Researchers developed an improved YOLOv5-based algorithm incorporating weighted image fusion to enhance detection and localization of underwater plastic waste in optical images, addressing challenges of noise, low contrast, and blurred textures in aquatic environments.
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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.