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WaveFilter: Advanced Imaging for Marine Microplastic Monitoring
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This paper describes WaveFilter, a deep-learning system based on the YOLOv5 model trained to automatically detect microplastics in images of aquatic environments, achieving about 80% precision in identifying plastic particles even against complex backgrounds. The model is compact enough for real-time deployment, offering a faster and more scalable alternative to tedious manual counting methods. Automated detection tools like this could make large-scale marine microplastic monitoring more practical and consistent.
YOLOv5 has proven to be an efficient deep learning model for the detection of microplastics in aquatic environments. It is compact, with a total number of 214 layers and 7,022,326 parameters, while its model size is approximately 14.4 MB. Extensive performance analysis was done that states that the model has a precision of 79.8% and a recall of 67.1%, hence proving efficiency in the detection of instances of microplastics. The performance metrics are that the model offers a mAP of 72.1% at IoU=0.5 and averaged mAP of 34.1% averaged across different IoU values. These also validate the strength of YOLOv5 to detect microplastic with complex backgrounds which could be applied to real-time, automated, computer vision-based frameworks for the detection of marine plastic pollution. These promising results from the study have opened further vistas for development in environment monitoring and conservation.
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Application of the YOLOv5 Model for the Detection of Microobjects in the Marine Environment
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Researchers trained the YOLOv5 machine learning model to automatically detect and classify microplastics and microplankton in underwater photo and video images in real time. The system achieved accuracy comparable to manual counting, offering a faster, less labor-intensive method for monitoring microplastic contamination in marine environments.
Implementation of YOLOv5 for Detection and Classification of Microplastics and Microorganisms in Marine Environment
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Researchers trained a YOLOv5 deep learning model on marine environment images and demonstrated it can accurately detect and classify both microplastics and microorganisms in real time, offering a memory-efficient tool for automated environmental monitoring.
Detecting Microplastics in Aquatic Bodies Using Object Detection Model
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Microplastics—tiny plastic particles that pollute water and can work their way up the food chain into the fish and seafood we eat—are hard to track using traditional methods. This research shows that AI-powered image recognition tools (specifically YOLO models, a type of "smart camera" software) can automatically spot and count microplastics in water much faster than manual methods, with a newer version performing better than its predecessor. Faster, more accurate detection means scientists and regulators could monitor plastic pollution more effectively, which is an important step toward understanding and reducing our exposure to these contaminants.
Real-time detection of macroplastic pollution in inland waters: development of a lightweight image recognition system
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This study developed a lightweight YOLOv8-based real-time detection system for macroplastic pollution in inland waters, optimized for deployment on fixed camera systems and UAVs. Pre-processing techniques including image tiling and blurring improved recall and mAP, and the system supports both decentralized on-site and centralized processing configurations.
An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
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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.
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.