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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.
The efficiency of using the YOLOV5 machine learning model for solving the problem of automatic de-tection and recognition of micro-objects in the marine environment is studied. Samples of microplankton and microplastics were prepared, according to which a database of classified images was collected for training an image recognition neural network. The results of experiments using a trained network to find micro-objects in photo and video images in real time are presented. Experimental studies have shown high efficiency, comparable to manual recognition, of the proposed model in solving problems of detect-ing micro-objects in the 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.
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.
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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.
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Researchers developed an improved computer vision model, called YOLO-CSM, to automatically detect and classify microplastics in images. In testing, the model identified microplastics more accurately than previous versions while still processing images quickly enough for real-time use.
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Researchers trained a YOLOv8 deep learning model on underwater debris imagery to enable real-time detection and classification of submerged plastic trash, demonstrating substantially faster and more scalable performance than traditional manual survey or sonar-based methods.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.