注意力U-Nets在舰艇自动探测中的适用性分析

IF 0.3 Q4 REMOTE SENSING
Pranshav Gajjar, Manav Garg, Vatsal Shah, Pooja Shah, Anup Das
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引用次数: 1

摘要

从航空图像中准确有效地检测船舶是一项有趣而困难的任务,具有极端的社会重要性,因为它们与海事违规和其他可疑行为有关。拥有一个具有所需功能的自动化系统表明,相关的表征和总体底层过程的工时大幅减少。随着各种图像处理技术的出现以及机器学习和深度学习领域的进步,可以为上述任务创建专门的方法。增强现有方法的一个直观方法是研究基于注意的认知,并利用可用的注意模块开发改进的神经结构。本文以U-Net和其他附属架构为基础,对各种注意力模块的效用进行了新颖的研究和实证分析,以实现计算高效和准确的船舶检测任务。在考虑其时间性能的同时,对表现最好的模型进行了全面的描述和解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applicability analysis of attention U-Nets over vanilla variants for automated ship detection
Abstract 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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来源期刊
自引率
28.60%
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5
审稿时长
12 weeks
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