We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Automated Marine Debris Detection and Segmentation Using Yolov11 and Deeplabv3 for Marine Imagery
AI summary Read the abstract
This study combined YOLOv11 for real-time object detection and DeepLabV3+ for semantic segmentation to autonomously detect and segment plastic marine waste from satellite imagery. The models were trained on the Marine Debris v49 dataset from Roboflow and MARIDA archives, using stratified splits and augmentation techniques to address challenges in monitoring dynamic marine plastic pollution environments.
Marine plastic debris represents a vital danger to ocean ecosystems, maritime navigation safety, and human lives.Traditional monitoring and cleanup techniques are time-consuming, labor-intensive, and often fail to capture the dynamic character of marine environments.This research introduces an autonomous deep learning system that combines YOLOv11 for real-time object detection and DeepLabV3+ for semantic segmentation of plastic marine waste from satellite images.The models learned on the Marine Debris v49 dataset, which was filtered from Roboflow and MARIDA archive, utilizing stratified splits to maintain class balance.The data preprocessing involved resizing, normalization, and augmentation techniques such as random rotation,
More Papers Like This
An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
AI summary Read the abstract
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.
Deep Learning Approaches for Ocean Waste Detection: A Comparative Study of YOLOv8 and Faster R-CNN
AI summary Read the abstract
Deep learning models (YOLOv8 and Faster R-CNN) were compared for detecting ocean waste, with Faster R-CNN achieving better precision for meso- and macroplastic detection, though both systems struggled with microplastics due to their tiny size and visual similarity to natural marine particles. This highlights the urgent need for specialized AI detection methods capable of identifying microplastics in ocean monitoring applications.
Deep-Feature-Based Approach to Marine Debris Classification
AI summary Read the abstract
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.
AnAI-PoweredSystemforReal-TimeUnderwaterPlastic Detection and Classification Using YOLOv7 and VGG16
AI summary Read the abstract
This study proposes a hybrid AI architecture combining YOLOv7 for real-time detection and VGG16 for detailed classification of underwater plastic waste, including bottles, bags, nets, and wrappers, with SMTP notification for automated alerts. The system demonstrates high accuracy and performance under challenging underwater conditions including turbidity and low visibility, offering a practical tool for marine waste monitoring and rapid response.
A machine learning-based detection, classification, and quantification of marine litter along the central east coast of India
AI summary Read the abstract
This study combined conventional field survey methods with a YOLOv5 machine learning model to detect and classify marine litter along the central east coast of India, identifying over 4,500 litter items and categorizing 37.5% of beaches as 'dirty' and 62.5% as 'extremely dirty.' The trained YOLOv5 model achieved an F1-score of 0.797 and mAP of 0.95, demonstrating AI-based approaches as effective complements to traditional marine litter monitoring.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.