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Papers
20 resultsShowing papers similar to TECI-YOLO: An Efficient, Lightweight Model for Detecting Small Floating Objects on Water Surfaces
ClearDetection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment
This paper is not directly about microplastic pollution. It presents an improved computer vision system (YOLOv5s) for detecting floating objects on water surfaces using edge computing. While such technology could potentially be applied to detect plastic debris in rivers, the study itself focuses on the object detection algorithm rather than microplastics specifically.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Identification and detection of microplastic particles in marine environment by using improved faster R–CNN model
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.
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 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.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
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.
On advances, challenges and potentials of remote sensing image analysis in marine debris and suspected plastics monitoring
This review evaluates the current state of satellite and aerial remote sensing for detecting marine plastic debris, noting that while progress has been made using optical and hyperspectral imaging, significant challenges remain including low detection resolution for small particles, confusion with other floating materials, and the need for better machine learning algorithms. The paper is relevant to the microplastics field as large-scale monitoring tools are needed to track plastic pollution distribution and inform cleanup and policy efforts, though direct detection of microplastics (<5 mm) from orbit remains largely out of reach with current technology.
Battle Models: Inception ResNet vs. Extreme Inception for Marine Fish Object Detection
Not relevant to microplastics — this paper compares two deep learning models (Inception ResNet and Xception) for detecting and classifying marine fish species in underwater images, with no connection to plastic pollution.
Towards Detecting Floating Objects on a Global Scale with Learned Spatial Features Using Sentinel 2
Researchers developed a machine learning approach using Sentinel-2 satellite imagery to detect floating plastic debris and marine litter on a global scale, demonstrating that learned spatial features can improve detection of large aggregations of floating objects on water surfaces.
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.