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Papers
61,005 resultsShowing papers similar to Efficient Object Detection of Marine Debris using Pruned YOLO Model
ClearTowards More Efficient EfficientDets and Real-Time Marine Debris Detection
Researchers improved the efficiency of a class of AI-based object detection systems called EfficientDets for real-time identification of marine debris underwater. Their optimized models achieved better accuracy while running faster, making them more practical for use on autonomous underwater vehicles. This technology could help enable automated detection and removal of ocean plastic waste, which breaks down into harmful microplastics over time.
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.
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.
TECI-YOLO: An Efficient, Lightweight Model for Detecting Small Floating Objects on Water Surfaces
Despite its title referencing floating object detection on water, this paper studies a machine learning model (TECI-YOLO) for detecting small objects on water surfaces using computer vision — not microplastic pollution. It examines improvements in detection accuracy and computational efficiency for real-time marine monitoring and is not directly relevant to microplastics research.
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.
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.
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.
Towards 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.
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.
An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
Researchers developed a deep learning-based coastal debris detection system using YOLOv7 and the SAHI vision library to identify microplastics in image data collected from shorelines. The system demonstrated effective detection performance and offers a scalable approach for automated monitoring of microplastic litter in coastal environments.
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.
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.
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.
WaveFilter: Advanced Imaging for Marine Microplastic Monitoring
This paper describes WaveFilter, a deep-learning system based on the YOLOv5 model trained to automatically detect microplastics in images of aquatic environments, achieving about 80% precision in identifying plastic particles even against complex backgrounds. The model is compact enough for real-time deployment, offering a faster and more scalable alternative to tedious manual counting methods. Automated detection tools like this could make large-scale marine microplastic monitoring more practical and consistent.
Full stage networks with auxiliary focal loss and multi-attention module for submarine garbage object detection
Researchers developed a new AI-based detection system using modified YOLO neural networks to identify and locate garbage objects on the seafloor in complex underwater images. The system uses multi-scale feature extraction and a specialized loss function to accurately detect small and deformed debris items, supporting robotic clean-up efforts in marine 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.
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.
Underwater Waste Recognition and Localization Based on Improved YOLOv5
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.
Detection and assessment of marine litter in an uninhabited island, Arabian Gulf: A case study with conventional and machine learning approaches
Researchers surveyed marine litter on a remote Arabian Gulf island after a large cleanup, then trained a YOLO-v5 deep learning model on 10,400 beach images to automatically detect debris, achieving 90% detection accuracy and demonstrating that windward shores accumulate significantly more litter from neighboring countries.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
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.
Projector deep feature extraction-based garbage image classification model using underwater images
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.
GoogLeNet-Based Deep Learning Framework for Underwater Microplastic Classification in Marine Environments
Researchers trained a GoogLeNet deep learning model on underwater images to classify microplastics into four categories, achieving strong classification performance for primary microplastics, secondary microplastics, non-microplastic debris, and marine biota in turbid coastal waters.