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IoT-Integrated Image Recognition System for Microplastic Detection and Classification
Summary
Researchers developed an IoT-based system that combines a microscopic camera with a YOLOv8 deep learning model to detect and classify microplastics in real time, including types like LDPE, PE, PHA, and PS. The system achieves high accuracy across diverse environmental conditions and visualizes data through a cloud-based dashboard. This scalable approach offers a practical tool for monitoring microplastic pollution, with potential for future integration on marine vessels.
Microplastic pollution threatens aquatic ecosystems and public health, requiring innovative detection solutions. This paper presents an IoT-based system combining a microscopic camera with a YOLOv8 model for real-time detection and classification of microplastics, including types like LDPE, PE, PHA, and PS. The system achieves high accuracy, robustness in diverse environmental conditions, and real-time data visualization through a cloud-based dashboard. Challenges like class imbalance and IoT hardware constraints were mitigated using model optimization and advanced preprocessing. This scalable solution offers a practical tool for monitoring microplastic pollution, with future potential for marine vessel integration and enhanced detection using advanced imaging and deep learning methods.
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