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Papers
61,005 resultsShowing papers similar to Towards More Efficient EfficientDets and Real-Time Marine Debris Detection
ClearTowards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detection
Researchers improved the computational efficiency of EfficientDet object detectors and applied them to real-time marine debris detection for autonomous underwater vehicles, achieving AP gains of 1.2-2.6% without increased GPU latency, creating a new in-water plastic bag and bottle dataset, and investigating detection performance degradation under low-light underwater conditions.
Efficient Object Detection of Marine Debris using Pruned YOLO Model
Researchers applied a pruned YOLOv4 object detection model to identify marine debris, including microplastic-contaminated items, in underwater images. Channel pruning reduced the model size by 90% while maintaining detection accuracy, making it suitable for deployment on autonomous underwater vehicles. The study demonstrates that efficient machine learning models can support real-time marine debris detection and cleanup efforts.
YOLOv8-C2f-Faster-EMA: An Improved Underwater Trash Detection Model Based on YOLOv8
Researchers improved an AI-based object detection system (YOLOv8) to better identify small pieces of underwater trash, achieving a 5% improvement in detection accuracy. Automated trash detection in waterways matters because removing plastic waste before it breaks into microplastics can reduce the amount of tiny plastic particles that eventually contaminate drinking water and seafood.
AI – Driven Marine Debris Detection for Ocean Conservation
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.
PBM‐YOLO: A Performance Balanced Floating Garbage Detection Model for Water Surface Environments
Researchers developed PBM-YOLO, a performance-balanced deep learning model for detecting floating garbage including plastic debris on water surfaces, optimising the architecture to balance detection accuracy and computational efficiency for practical deployment in ecological protection and waterway resource recycling applications.
Improving YOLOv11 for marine water quality monitoring and pollution source identification
Researchers improved the YOLOv11 computer vision model to better detect and identify marine pollution sources, including oil spills, debris, and turbid water, in complex underwater environments. The enhanced model achieved higher detection accuracy and faster processing speeds compared to the standard version. The study demonstrates that advanced AI-based monitoring tools can meaningfully improve our ability to track and respond to marine pollution in real time.
Enhancing marine debris identification with convolutional neural networks
A deep learning model was developed to identify and classify marine debris components captured by underwater remotely operated vehicle imagery, addressing the challenge of widely distributed ocean waste including microplastics. The convolutional neural network demonstrated improved accuracy for debris detection and classification compared to conventional image analysis methods.
Smart Ocean Cleanup: An AI-Integrated Autonomous System for Marine Waste Management
This paper presents an AI-powered autonomous boat system designed to detect and collect marine pollution — including plastics, oil spills, and microplastics — using deep learning image classification, IoT sensors, and robotic collection mechanisms. The system demonstrated over 94% accuracy for pollutant detection and classification across several AI models. While focused more broadly on ocean cleanup technology than on microplastic science specifically, it demonstrates how AI-integrated robotics could help address the practical challenge of removing plastic waste from ocean surfaces before it breaks down further.
Automatic Detection of Microplastics in the Aqueous Environment
Researchers developed a deep-learning system for real-time detection and counting of microplastics in freshwater, achieving high accuracy for particles 1 mm and larger.
FindingPlastic: Underwater Plastic Bag Detection and Retrieval
Engineers developed an automated system using artificial intelligence to detect, track, and capture floating plastic bags underwater before they break down into microplastics. The system combines modern object detection and tracking algorithms and was successfully tested in a tank environment, offering a potential tool for robotic ocean cleanup efforts.
Real-Time Detection of Microplastics Using an AI Camera
Researchers developed a camera-based system using artificial intelligence to detect and measure microplastics in real time as they move through water. The system was tested with three different camera setups and could identify particles, measure their size, and track their speed. This technology could provide a faster and more practical alternative to the labor-intensive laboratory methods currently used to monitor microplastic pollution.
Development of Drifting Debris Detection System using Deep Learning on Coastal Cleanup
Researchers developed a deep learning-based system to detect litter on beaches using images and automated object recognition. Efficient litter detection tools could help coastal cleanup programs identify and remove plastic debris before it breaks down into microplastics.
Deep-Sea Debris Identification Using Deep Convolutional Neural Networks
Researchers developed a deep convolutional neural network classifier to identify and distinguish deep-sea debris from seafloor imagery, demonstrating that automated AI-based detection can support submersible clean-up operations targeting marine debris in deep-sea environments.
An automated solid waste detection using the optimized YOLO model for riverine management
Researchers developed an optimized YOLO-based deep learning model for automated detection of solid waste in rivers, achieving high accuracy in identifying floating debris to support autonomous robotic riverine cleanup systems.
Plastic Waste on Water Surfaces Detection Using Convolutional Neural Networks
Researchers evaluated state-of-the-art convolutional neural network architectures for automatically detecting plastic waste on water surfaces, training models on a dataset representing four categories of plastic litter including plastic bags. The study benchmarked multiple CNN object detection models following extensive dataset preprocessing to determine the most effective approach for automated plastic pollution identification.
Automatic Identification and Classification of Marine Microplastic Pollution Based on Deep Learning and Spectral Imaging Technology
Researchers developed an AI system combining deep learning with multispectral imaging to automatically identify and classify marine microplastics, using a feature-selection method called ReliefF to reduce noise in complex ocean samples. The approach achieved high accuracy and offers a scalable solution for large-scale ocean microplastic monitoring that outperforms traditional manual inspection.
An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment
Researchers developed an improved object detection algorithm based on Mask R-CNN with dilated convolution in the feature pyramid network to enable more accurate automated identification and segmentation of marine garbage by underwater detectors. The embeddable algorithm is designed to function effectively in complex, low-resolution underwater environments.
A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
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
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.
Aquatic Trash Detection and Classification: a Machine Learning and Deep Learning Perspective
This review examines machine learning and deep learning approaches for detecting and classifying aquatic trash in waterways, evaluating how computer vision algorithms trained on underwater and surface imagery can automate pollution monitoring for faster, more scalable ocean cleanup.
Underwater and airborne monitoring of marine ecosystems and debris
Researchers demonstrated that the deep-learning object detection algorithm YOLO v3 can detect underwater sea life and floating marine debris with mean average precision of 69.6% and 77.2% respectively, using autonomous underwater and aerial robots. The study proposes this approach as a foundation for scalable autonomous monitoring systems capable of tracking marine ecosystems and plastic debris across oceanographic scales.
Slim Deep Learning Approach for Microplastics Image Classification in the Marine Environment
Researchers developed a lightweight convolutional neural network called the Slim-DL-Model for classifying microplastics in marine environment images, designed to overcome the computational demands of existing architectures like VGG16 and ResNet for real-time field applications. The model achieves competitive classification accuracy while significantly reducing computational requirements, enabling deployable microplastic monitoring systems.
Detection of Trash in Sea Using Deep Learning
Researchers developed a deep learning convolutional neural network (CNN) model to detect and classify trash in marine and aquatic environments from underwater images, aiming to overcome the limitations of manual debris detection for objects that may be submerged or partially obscured.
Implementation of YOLOv5 for Detection and Classification of Microplastics and Microorganisms in Marine Environment
Researchers trained a YOLOv5 deep learning model on marine environment images and demonstrated it can accurately detect and classify both microplastics and microorganisms in real time, offering a memory-efficient tool for automated environmental monitoring.