We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Automated Plastic Waste Detection Using Advanced Deep Learning Frameworks
AI summary Read the abstract
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.
The increasing accumulation of plastic waste poses one of the most significant threats to both human health and the environment. In recent years, microplastics have been detected ubiquitously—in food, water, and even the air—emphasizing the pressing need for efficient control and monitoring methods. To address this challenge, this study presents a deep learning–based system for automated plastic waste detection from images, with the objective of improving accuracy and efficiency in identifying plastic materials. For experimentation, we used WPRD dataset (combined of WaDaBa and the Plastic Recyclable Detection dataset) and employed the YOLOv11 (You Only Look Once) object detection framework. Five major categories of plastic waste were considered: Polyethylene Terephthalate (PET), High-Density Polyethylene (PEHD), Polypropylene (PP), Polystyrene (PS), and Ultra-High Temperature (UHT) packaging. The system demonstrates strong performance, achieving high precision and recall in classifying plastic objects within diverse environmental contexts. Specifically, the YOLOv11m architecture attained a mean Average Precision (mAP) of 94.8%, underscoring the effectiveness of the proposed approach for automated plastic waste detection.
More Papers Like This
Deep transfer learning benchmark for plastic waste classification
AI summary Read the abstract
Researchers benchmarked six deep transfer learning models for classifying plastic waste types, achieving high accuracy in automated sorting that could help address plastic pollution by improving recycling efficiency.
Advancing Plastic Waste Classification and Recycling Efficiency: Integrating Image Sensors and Deep Learning Algorithms
AI summary Read the abstract
Researchers developed a deep learning approach combined with image sensors to improve plastic waste classification and recycling efficiency. The study demonstrates that this method can distinguish between chemically similar plastics like PET and PET-G that conventional near-infrared spectroscopy struggles to differentiate, potentially improving automated sorting systems.
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.
Image-Based Detection and Deep Learning Methods for Plastic and Environmental Monitoring
AI summary Read the abstract
Researchers trained computer programs to sort waste photos from the TrashNet image dataset into "harmful" (plastic, mixed trash) and "less harmful" (paper, cardboard, glass, metal) categories. One model, based on VGG16, correctly classified images more than 82% of the time, suggesting such tools could help automate detection of plastic pollution.
Plastic Waste on Water Surfaces Detection Using Convolutional Neural Networks
AI summary Read the abstract
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.
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.