Ocean Surface Pollution Detection: Applicability Analysis of V-Net with Data Augmentation for Oil Spill and Other Related Ocean Surface Feature Monitoring

Mehta, Naishadh; Shah, Pooja; Gajjar, Pranshav; Ukani, Vijay

doi:10.1007/978-981-19-2130-8_2

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Advancements in computational intelligence have paved the way for the development of surrogate or predictive systems, which exhibit superior accuracy levels [21][22][23][24]. These developments span various domains, yielding heuristic implications and observable utility [25].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

Garg,

Gajjar,

Shah

et al. 2023

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The musical key serves as a crucial element in a piece, offering vital insights into the tonal center, harmonic structure, and chord progressions while enabling tasks such as transposition and arrangement. Moreover, accurate key estimation finds practical applications in music recommendation systems and automatic music transcription, making it relevant across academic and industrial domains. This paper presents a comprehensive comparison between standard deep learning architectures and emerging vision transformers, leveraging their success in various domains. We evaluate their performance on a specific subset of the GTZAN dataset, analyzing six different deep learning models. Our results demonstrate that DenseNet, a conventional deep learning architecture, achieves remarkable accuracy of 91.64%, outperforming vision transformers. However, we delve deeper into the analysis to shed light on the temporal characteristics of each deep learning model. Notably, the vision transformer and SWIN transformer exhibit a slight decrease in overall performance (1.82% and 2.29%, respectively), yet they demonstrate superior performance in temporal metrics compared to the DenseNet architecture. The significance of our findings lies in their contribution to the field of musical key estimation, where accurate and efficient algorithms play a pivotal role. By examining the strengths and weaknesses of deep learning architectures and vision transformers, we can gain valuable insights for practical implementations, particularly in music recommendation systems and automatic music transcription. Our research provides a foundation for future advancements and encourages further exploration in this area.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

Garg,

Gajjar,

Shah

et al. 2023

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“…The study, as presented in Morillas et al (2015), indicates that the use of breaking a high-dimensional Synthetic Aperture Radar (SAR) data sample into multiple fixed-size images was present and further offered a predictive analysis by leveraging the Support Vector Machine (SVM) methodology. In the paper by Mehta et al (2022), the author proposed the use of V-Nets and augmentative approaches for a related field of oil spill detection and remote sensing. Another set of architectures that has shown utility in statements concerning associated domains and object detection is the Feature Pyramid Network, as evidenced by Shamsolmoali et al (2021) and Zareapoor et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

Gajjar

Garg

Shah

et al. 2022

Reports on Geodesy and Geoinformatics

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Accurate and efficient detection of ships from aerial images is an intriguing and difficult task of extreme societal importance due to their implication and association with maritime infractions, and other suspicious actions. Having an automated system with the required capabilities indicates a substantial reduction in the related man-hours of characterization and the overall underlying processes. With the advent of various image processing techniques and advancements in the field of machine learning and deep learning, specialized methodologies can be created for the said task. An intuition for the enhancement of existing methodologies would be a study on attention-based cognition and the development of improved neural architectures with the available attention modules. This paper offers a novel study and empirical analysis of the utility of various attention modules with U-Net and other subsidiary architectures as a backbone for the task of computationally efficient and accurate ship detection. The best performing models are depicted and explained thoroughly, while considering their temporal performance.

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Conditional Generative Adversarial Networks for SAR-based Ocean Feature Monitoring

Gajjar,

Mehta,

Patel

et al. 2024

Procedia Computer Science

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Ocean Surface Pollution Detection: Applicability Analysis of V-Net with Data Augmentation for Oil Spill and Other Related Ocean Surface Feature Monitoring

Cited by 3 publications

References 32 publications

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation

Applicability analysis of attention U-Nets over vanilla variants for automated ship detection

Conditional Generative Adversarial Networks for SAR-based Ocean Feature Monitoring

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