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61,005 resultsShowing papers similar to A Comprehensive Review of Deep Learning Algorithms for Underwater Trash Detection: Advancements, Challenges, and Future Directions
ClearA 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.
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 Image Detection for Cleaning Purposes; Techniques Used for Detection Based on Machine Learning
Researchers reviewed machine learning techniques for underwater image detection to support water pollution cleanup, focusing on convolutional neural networks and region-based CNN methods for identifying surface mucilage and debris. The study evaluated supervised classification algorithms as the most effective approach for automated aquatic waste detection systems.
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.
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.
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.
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.
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.
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.
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.
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.
Detection and Recognition of Ocean Garbage Using DIY ROV-Mounted DNN-Based Classification of Laser Images
Researchers designed a low-cost DIY underwater robot equipped with a laser imaging system and deep learning classifier to detect and categorize underwater garbage from microplastics to large debris. A custom-trained convolutional neural network achieved 91% classification accuracy, outperforming transfer learning approaches.
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.
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.
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.
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.
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.
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.
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.
Amphitrite v1.0: An underwater database for marine debris and biodiversity
Researchers created Amphitrite v1.0, an underwater database of 16 labeled categories of marine debris and biodiversity images suitable for training computer vision models for automated marine litter detection, validated against the latest YOLO object recognition architectures.
Up-to-Date Scoping Review of Object Detection Methods for Macro Marine Debris
This review examines over two decades of research into object detection methods for identifying macro marine debris, with a focus on deep learning advances made in the past five years. Following PRISMA-ScR guidelines, the authors assessed current techniques and identified key progress and remaining gaps in automated marine litter identification systems.
Review of Methods for Automatic Plastic Detection in Water Areas Using Satellite Images and Machine Learning
This review surveys methods for automatically detecting floating plastic pollution in water using satellite imagery and machine learning. The study describes key data acquisition techniques and deep learning algorithms being developed to identify plastic accumulation zones, track waste movement, and help address ocean plastic pollution more effectively.