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Detecting Microplastics in Aquatic Bodies Using Object Detection Model
Summary
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.
Microplastics (MPs) are emerging as a ubiquitous contaminant in several ecosystems and today are characterized as emerging contaminants due to their omnipresence, leaching, and absorption of chemicals, dynamic uptake by aquatic living organisms, accompanied by the nonexistence of effective environmental protection strategies. MP pollution is a threat to the aquatic biotic species, so the detection of MPs and prevention play a crucial role in environmental monitoring and management. The detection of MPs can be accelerated using a deep learning technique that is both time-saving and efficient. The YOLO (You Only Look Once) model, a convolutional neural network- (CNN-) based approach, is an ideal tool for detecting and quantifying MP pollution in various environments. In this chapter, we review the current state-of-the-art MP detection methods and their applications. Yolo versions 5 and 6 are used to compare the accuracy, recall, and precision and YOLOv6 performed better than YOLOv5.