We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Modified Adaptive Receptive Field Feature Extraction with Gated Fusion Adaptive Graph Neural Network for Microplastics Detection in Underwater Resources
AI summary Read the abstract
A deep learning model combining progressive GAN preprocessing, Decoupled Faster R-CNN detection, and a Gated Fusion Adaptive Graph Neural Network classified microplastic images from underwater resources into fiber, film, fragment, and pellet types with 96.60% accuracy. This automated detection framework significantly advances scalable, reliable identification of microplastics in complex aquatic environmental samples.
Microplastics are tiny pieces of plastic waste that have accumulated worldwide in the location. Analyzing environmental sample images is difficult because the complexity of real data renders either automatic and manual analysis unreliable or time demanding. As a result, the proposed a spatial-temporal deep learning model Gated Fusion Adaptive Graph Neural Network (GFAGNN) is utilized to capture spatial features for detecting microplastics in underwater resources. Initially, microplastic images in water resources are gathered and then preprocessed using Progressive Wasserstein Generative Adversarial Network (PWGAN) and Edge-Aware Interactive Contrast Enhancement (e-IceNet) to reduce the noise and improve color improvement in microplastic images. After that, the next phase is object detection, the parts of a water image having microplastics are detected using a Decoupled Faster R-CNN - Multi-Scale Attention Mechanism (DeFRCNN-MAM). Using the Modified Adaptive Receptive Field Feature Extraction (MARFFE), extracts to detention the consistency of designs. The attributes are then input for Gated Fusion Adaptive Graph Neural Network (GFAGNN) in order to detect the image as "Fiber", "Film", "Fragment" or "Pallet" dependent on the plastics state. The suggested approach forecasts microplastics in underwater resources with an accuracy of 96.60%, a PPV of 94.20%, and FDR of 5.80%, correspondingly. Therefore, the suggested technique offers a thorough comprehension of all methods for identifying microplastics in order to decrease the possible impact on aquatic ecosystems.
More Papers Like This
Enhancing marine debris identification with convolutional neural networks
AI summary Read the abstract
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.
GoogLeNet-Based Deep Learning Framework for Underwater Microplastic Classification in Marine Environments
AI summary Read the abstract
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.
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.
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.
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.
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.