Absorbed dose rate for marine biota due to the oil spilled using ICRP reference animal and Monte Carlo simulation

Lopes, José Marques; Lentini, Carlos A. D.; Mendonça, Luís F.F.; Lima, André T. Cunha; Vasconcelos, Rodrigo Nogueira de; Silva, Ademir X.; Porsani, Milton J.

doi:10.1016/j.apradiso.2022.110354

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…The consequences are not limited to marine life; they also encompass critical ecosystem functions [9,10]. Furthermore, the presence of toxic chemicals in spilled oil can cause direct fatality or reproductive impairments in aquatic fauna, such as fish, when accidentally ingested [11,12]. The resulting substantial decline in their populations has significant ramifications for the fishing industry.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

Li,

Yang,

Yang

et al. 2024

Remote Sensing

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The efficient and timely identification of oil spill areas is crucial for ocean environmental protection. Synthetic aperture radar (SAR) is widely used in oil spill detection due to its all-weather monitoring capability. Meanwhile, existing deep learning-based oil spill detection methods mainly rely on the classical U-Net framework and have achieved impressive results. However, SAR images exhibit high noise, blurry boundaries, and irregular shapes of target areas, as well as speckles and shadows, which lead to the loss of performance in existing algorithms. In this paper, we propose a novel network architecture to achieve more precise segmentation of oil spill areas by reintroducing rich semantic contextual information before obtaining the final segmentation mask. Specifically, the proposed architecture can re-fuse feature maps from different levels at the decoder end. We design a multi-convolutional layer (MCL) module to extract basic feature information from SAR images, and a feature extraction module (FEM) module further extracts and fuses feature maps generated by the U-Net decoder at different levels. Through these operations, the network can learn rich global and local contextual information, enable sufficient interaction of feature information at different stages, enhance the model’s contextual awareness, and improve its ability to recognize complex textures and blurry boundaries, thereby enhancing the segmentation accuracy of SAR images. Compared to many U-Net based segmentation networks, our method shows promising results and achieves state-of-the-art performance on multiple evaluation metrics.

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Section: Introductionmentioning

confidence: 99%

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

Li,

Yang,

Yang

et al. 2024

Remote Sensing

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“…Oil spills in the ocean have become one of the most significant environmental issues in modern times [1][2][3][4][5][6]. These spills cause severe damage to ecosystems, biodiversity, and the loss of essential ecosystem processes [7,8]. Furthermore, the impact of oil spills is not limited to the environment, as they can also adversely affect the economy and public health [9,10].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges

Vasconcelos

Lima

Lentini

et al. 2023

JMSE

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Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its impact on coastal and marine ecosystems. A novel approach was employed in this study to evaluate the scientific literature in this field through bibliometric analysis and literature review. The Scopus database was used to evaluate the relevant scientific literature in this field, followed by a bibliometric analysis to extract additional information, such as architecture type, country collaboration, and most cited papers. The findings highlight significant advancements in oil detection at sea, with a strong correlation between technological evolution in detection methods and improved remote sensing data acquisition. Multilayer perceptrons (MLP) emerged as the most prominent neural network architecture in 11 studies, followed by a convolutional neural network (CNN) in 5 studies. U-Net, DeepLabv3+, and fully convolutional network (FCN) were each used in three studies, demonstrating their relative significance too. The analysis provides insights into collaboration, interdisciplinarity, and research methodology and contributes to the development of more effective policies, strategies, and technologies for mitigating the environmental impact of oil spills in OSDMDL.

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Internal dose rate due to intake of uranium and thorium by fish from a dam reservoir associated with a uranium mine in Brazil

de Souza Pereira,

Kelecom,

Lopes

et al. 2024

Radiat Environ Biophys

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Absorbed dose rate for marine biota due to the oil spilled using ICRP reference animal and Monte Carlo simulation

Cited by 5 publications

References 45 publications

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

A Novel Multi-Scale Feature Map Fusion for Oil Spill Detection of SAR Remote Sensing

Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges

Internal dose rate due to intake of uranium and thorium by fish from a dam reservoir associated with a uranium mine in Brazil

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