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Papers
61,005 resultsShowing papers similar to A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data
ClearHybrid Deep Learning Approach for Marine Debris Detection in Satellite Imagery Using UNet with ResNext50 Backbone
Despite its title referencing marine debris detection, this paper develops a deep learning computer vision model for identifying marine debris in satellite imagery using a UNet architecture with a ResNext50 backbone — not a study of microplastic pollution itself. It is a remote sensing and machine learning engineering paper, and while the technology could support large-scale ocean plastic monitoring, the paper does not directly examine microplastics or their health effects.
Evaluation of residual plastic film pollution in pre-sowing cotton field using UAV imaging and semantic segmentation
Researchers proposed a UAV-based imaging method combined with a modified U-Net semantic segmentation model to evaluate residual plastic film pollution in pre-sowing cotton fields, collecting images from different heights under varying weather conditions to accurately map mulch film remnants.
Detection of Microplastics in Coastal Environments Based on Semantic Segmentation
Researchers developed a deep learning semantic segmentation approach for detecting microplastics on sandy beaches at the pixel level, evaluating 12 models including U-Net variants and transformer architectures under real-world conditions.
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.
UAV Approach for Detecting Plastic Marine Debris on the Beach: A Case Study in the Po River Delta (Italy)
UAV imaging was used to detect and map anthropogenic marine debris on beaches in the Po River Delta, Italy, testing different image processing strategies and demonstrating that centimeter-scale spatial resolution UAV surveys can efficiently locate macroplastics before they degrade into harder-to-remove microplastics.
Object Detection of Macroplastic Waste Using Unmanned Aerial Vehicles in Urban Canal
Researchers developed and tested an unmanned aerial vehicle-based system for detecting macroplastic waste along riverbanks and beaches using object detection algorithms. The system achieved reliable detection performance and offers a scalable tool for large-area plastic litter surveys.
Use of UAVs and Deep Learning for Beach Litter Monitoring
Researchers developed an autonomous beach litter monitoring pipeline using UAV drone surveys combined with a YOLOv5 deep learning object detection algorithm trained on footage from Malta, Gozo, and the Red Sea coast. The system achieved a mean average precision (mAP50-95) of 0.252 across all litter classes and incorporated geolocation and digital elevation model data to support future autonomous retrieval robots.
Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods
Researchers developed an object-oriented machine learning classification strategy using unmanned aerial system imagery to automatically identify and quantify marine macro litter on sandy beaches, comparing three automated methods against manual counts. The UAS-based approach demonstrated capacity for scalable, cost-effective beach litter monitoring to support coastal pollution surveillance programs.
Deep learning approach for automatic microplastics counting and classification
Researchers developed a deep learning architecture combining U-Net segmentation and VGG16 classification to automatically count and categorise microplastic particles of 1-5 mm into fragments, pellets, and lines from digital camera images. The system reduces the cost and time of traditional microplastic quantification methods while enabling high-throughput monitoring.
Deep-Feature-Based Approach to Marine Debris Classification
This study applied deep learning to classify marine debris from images, demonstrating that feature-based neural network approaches can effectively distinguish plastic types and other debris categories to support automated ocean monitoring.
Mini Uav-based Litter Detection on River Banks
Researchers developed a drone-based litter detection system combining high-resolution mapping, deep learning object detection, and vision-based localization that locates riverbank litter with decimeter-level accuracy, enabling automated monitoring of plastic pollution in urban waterway areas.
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.
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.
Microplastic Binary Segmentation with Resolution Fusion and Large Convolution Kernels
Researchers developed an improved machine-learning model to automatically detect and segment microplastic particles in images, achieving better accuracy than previous approaches by combining multi-resolution image analysis with large convolution kernels. Reliable automated detection tools are essential for scaling up microplastic monitoring, since manual identification is too slow and inconsistent for the volumes of environmental samples that need to be processed.
Identification of Plastic Mulch in Cotton Fields Using UAV-Based Hyperspectral Data and Deep Learning Semantic Segmentation
Plastic mulch film is widely used in agriculture to improve crop yields, but residual plastic in fields contributes to soil microplastic contamination, and identifying where it remains after harvest is difficult at scale. This study used drone-mounted hyperspectral cameras combined with deep-learning image analysis to map plastic mulch coverage in cotton fields in China, achieving up to 80% accuracy in distinguishing plastic from soil and crop canopy. Accurate mapping of residual mulch is a critical first step toward targeted plastic removal and reducing the flow of agricultural microplastics into soil and water.
UAV imaging and deep learning based method for predicting residual film in cotton field plough layer
Researchers developed a method combining UAV imaging with three deep learning frameworks (LinkNet, FCN, and DeepLabv3) to segment and predict residual plastic film content in the plough layer of cotton fields, offering a lower-cost and higher-efficiency alternative to traditional manual sampling for agricultural plastic pollution monitoring.
Automated Plastic Waste Detection Using Advanced Deep Learning Frameworks
Researchers developed a deep learning system using advanced neural network frameworks for automated detection and classification of plastic waste from images, achieving high accuracy in identifying multiple plastic types to support environmental monitoring and waste sorting.
Computer vision segmentation model—deep learning for categorizing microplastic debris
Researchers developed a deep learning computer vision model for automatically categorizing beached microplastic debris from images. The segmentation model was trained to identify and classify different types of microplastic particles, reducing the need for time-consuming manual counting and laboratory analysis. The study suggests that automated image-based detection could enable more scalable and consistent monitoring of microplastic pollution along coastlines.
Reducing SpectralConfusion in Microplastic Analysis:A U‑Net Deep Learning Approach
Researchers developed a U-Net deep learning model to address spectral confusion between polyethylene and fatty acids in Raman spectroscopy-based microplastic detection, training the model on spectra from polystyrene, polyethylene, stearic acid, oleic acid, fatty acid mixtures, and polypropylene. The model achieved precise classification and, combined with binarization techniques, offered scalable qualitative and quantitative analysis of microplastics in complex environmental samples.
Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)
Researchers developed APLASTIC-Q, a convolutional neural network system trained on very high-resolution aerial imagery from Cambodia, capable of detecting, classifying, and quantifying floating and washed-ashore plastic litter — providing a scalable tool for remote monitoring of aquatic plastic pollution.
Microplastic Image Segmentation for Edge Devices using Lightweight U-Net
Researchers developed a lightweight U-Net model for microplastic image segmentation deployable on portable edge devices, testing depthwise separable convolution layers to maintain detection accuracy while reducing computational load. Using depthwise separable convolutions achieved similar mean intersection-over-union performance to the standard U-Net while reducing operations by 82.6%.
Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R
Researchers developed a convolutional neural network-based algorithm to automatically detect and quantify floating marine macro-litter in aerial images, training it on 3,723 images and integrating it into a web application for practical monitoring use.
Reducing Spectral Confusion in Microplastic Analysis: A U-Net Deep Learning Approach
A common problem in microplastic detection using Raman spectroscopy is that fatty acids in environmental samples look chemically similar to polyethylene (a common plastic), causing misidentification. This study trained a deep learning model (U-Net architecture) to distinguish polyethylene from fatty acids and other organic compounds based on subtle spectral differences, achieving accurate classification. Better detection methods are foundational to all microplastic research, and this AI-assisted approach could reduce false positives in environmental monitoring.
Developing Beach Litter Monitoring System Based on Reflectance Characteristics and its Abundance
Researchers developed a beach litter monitoring system using optical reflectance characteristics of plastic debris, training a remote sensing model to detect and classify litter items on sandy beach surfaces. The system demonstrated accurate detection of common plastic litter types and offers a scalable, automated alternative to manual beach surveys.