Implementation of YOLOv5 for Detection and Classification of Microplastics and Microorganisms in Marine Environment

Shishkin, Iurii E.; Grekov, A.N.

doi:10.1109/smartindustrycon57312.2023.10110736

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The deep learning algorithm enables faster detection of particles carried by water while minimizing the possibility of incorrect detection by non-experts. Some studies utilize traditional detection methods and deep learning algorithms to detect microplastics in soil and water (Ai et al 2023;Shishkin and Grekov 2023). Nevertheless, the direct use of cameras and microscopes in these detection methods does not offer a comprehensive solution, resulting in economical and practical limitations.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Microplastic Detection and Classification System Supported by Deep Learning Algorithm

Dal,

Kilic

2024

Preprint

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Microparticles are challenging to detect due to their small size and can harm living things when exposed. Especially microplastics are one of the harmful microparticles. For this reason, detecting microplastics in a vital consumer item such as water is essential. Machine learning in the detection method allows the learning of different types and sizes of microplastics, allowing such systems to work unremittingly in real time. The present study has designed a low-budget, high-accuracy device with a deep learning algorithm that can autonomously classify microplastics according to their size and type. Three lasers with dual beam wavelengths of 405nm, 655nm, and 534nm-807nm, frequently used in laser pointers, are light sources in the sensor. The beams formed by the lasers were combined employing a beam combiner, allowing beams to emerge from a single point. Classification success of up to 100% has been achieved, thanks to the different interference patterns of light sources of various wavelengths. 10µm polystyrene, 8µm polystyrene, and 8µm melamine prepared in different constancy were used as samples in the experiments.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Microplastic Detection and Classification System Supported by Deep Learning Algorithm

Dal,

Kilic

2024

Preprint

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High-throughput microplastic assessment using polarization holographic imaging

Li,

Zhu,

Huang

et al. 2024

Sci Rep

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Microplastic (MP) pollution has emerged as a global environmental concern due to its ubiquity and harmful impacts on ecosystems and human health. MP assessment has therefore become increasingly necessary and common in environmental and experimental samples. Microscopy and spectroscopy are widely employed for the physical and chemical characterization of MPs. However, these analytical methods often require time-consuming pretreatments of samples or expensive instrumentation. In this work, we develop a portable and cost-effective polarization holographic imaging system that prominently incorporates deep learning techniques, enabling efficient, high-throughput detection and dynamic analysis of MPs in aqueous environments. The integration enhances the identification and classification of MPs, eliminating the need for extensive sample preparation. The system simultaneously captures holographic interference patterns and polarization states, allowing for multimodal information acquisition to facilitate rapid MP detection. The characteristics of light waves are registered, and birefringence features are leveraged to classify the material composition and structures of MPs. Furthermore, the system automates real-time counting and morphological measurements of various materials, including MP sheets and additional natural substances. This innovative approach significantly improves the dynamic monitoring of MPs and provides valuable information for their effective filtration and management.

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The Diversity of Artificial Intelligence Applications in Marine Pollution: A Systematic Literature Review

Ning,

Pang,

Arifin

et al. 2024

JMSE

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Marine pollution, a major disturbance to the sustainable use of oceans, is becoming more prevalent around the world. Multidimensional and sustainable ocean governance have become increasingly focused on managing, reducing, and eliminating marine pollution. Artificial intelligence has been used more and more in recent years to monitor and control marine pollution. This systematic literature review, encompassing studies from the Web of Science and Scopus databases, delineates the extensive role of artificial intelligence in marine pollution management, revealing a significant surge in research and application. This review aims to provide information and a better understanding of the application of artificial intelligence in marine pollution. In marine pollution, 57% of AI applications are used for monitoring, 24% for management, and 19% for prediction. Three areas are emphasized: (1) detecting and responding to oil pollution, (2) monitoring water quality and its practical application, and (3) monitoring and identifying plastic pollution. Each area benefits from the unique capabilities of artificial intelligence. If the scientific community continues to explore and refine these technologies, the convergence of artificial intelligence and marine pollution may yield more sophisticated solutions for environmental conservation. Although artificial intelligence offers powerful tools for the treatment of marine pollution, it does have some limitations. Future research recommendations include (1) transferring experimental outcomes to industrial applications in a broader sense; (2) highlighting the cost-effective advantages of AI in marine pollution control; and (3) promoting the use of AI in the legislation and policy-making about controlling marine pollution.

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Implementation of YOLOv5 for Detection and Classification of Microplastics and Microorganisms in Marine Environment

Cited by 3 publications

References 16 publications

Design and Implementation of a Microplastic Detection and Classification System Supported by Deep Learning Algorithm

Design and Implementation of a Microplastic Detection and Classification System Supported by Deep Learning Algorithm

High-throughput microplastic assessment using polarization holographic imaging

The Diversity of Artificial Intelligence Applications in Marine Pollution: A Systematic Literature Review

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