We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Uso de YOLOv8 y aumento de datos para la detección de microplásticos en agua
AI summary Read the abstract
Researchers trained a YOLOv8 computer vision model on 500 stereoscopic microscope images of microplastics collected from two Mexican water bodies, finding that applying data augmentation techniques improved detection precision to 0.825 and mAP@50 to 0.73, outperforming training without augmentation.
Los microplásticos presentes en el agua se han convertido en un problema ambiental grave, por lo que ha surgido la necesidad de desarrollar técnicas y herramientas para su detección y posterior eliminación. En este estudio, se utilizó el modelo de visión por computadora YOLO (You Only Look Once) en su versión 8, para la detección de microplásticos en agua, el cual fue entrenado utilizando un conjunto de 500 imágenes obtenidas mediante microscopio estereoscópico, derivadas de muestras recolectadas en dos zonas del Estado de México: Valle de Bravo y el municipio de Malinalco. Adicionalmente a este conjunto de imágenes se le aplicaron diferentes técnicas de aumento de datos, incluyendo, Resize, Crop, Flip, Brightness and Contrast, HSV, Gamma Correction, CLAHE, RGB Shift y Gaussian Blur, con el objetivo de generar un conjunto más diverso. Los resultados obtenidos indican que YOLO, entrenado con el conjunto aumentado, alcanzó una precisión de 0.825 y un mAP@50 de 0.73, valores superiores a los logrados con el conjunto original sin aumentos. En conclusión, YOLO, en combinado con técnicas de aumento de datos, muestra ser eficaz para la detección de microplásticos, sentando las bases para su aplicación práctica en el monitoreo de cuerpos de agua.
More Papers Like This
Detecting Microplastics in Aquatic Bodies Using Object Detection Model
AI summary Read the abstract
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.
YOLOv8-Based Microplastic Detection and Quantification in River Water Microscopic Images
AI summary Read the abstract
Researchers trained a YOLOv8 deep learning model on 300 microscopic images of river water from three Indonesian sites, achieving 0.786 precision and 0.731 mAP@0.5 for detecting microplastics, with higher image resolution improving accuracy and FTIR laboratory validation confirming compositional agreement with model detections.
YOLOv7-Based Microplastic Detection: Crafting a Custom Dataset for Environmental Analysis
AI summary Read the abstract
Researchers used three versions of the YOLO object detection model to detect and count microplastics from a custom-built dataset. YOLOv8 achieved the highest overall accuracy at 81.4%, followed by YOLOv7 at 80.7% and YOLOv9 at 77.2%, though YOLOv7 performed best with real-time test data. The study demonstrates the potential of AI-based detection systems for automating microplastic identification in environmental samples.
Remote Microplastic Detector for Water Bodies
AI summary Read the abstract
This study investigated the infiltration of synthetic microfibers into streambed sediments, examining how textile fibers deposited on stream surfaces are transported and retained in hyporheic zones. The findings reveal that streambeds act as long-term sinks for microfiber pollution, potentially affecting benthic invertebrate communities.
Remote Microplastic Detector for Water Bodies
AI summary Read the abstract
This research investigated the formation of biofilms containing benzophenone-3 on microplastic surfaces, examining how the UV-filtering chemical accumulates with plastic particles. The study reveals that microplastics act as accumulation sites for common chemical additives, potentially concentrating harmful compounds in environmental and biological systems.
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.