Frontiers | Multi-Modal Remote Perception Learning for Object Sensory Data

IF 2.6 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Neurorobotics Pub Date : 2024-08-26 DOI:10.3389/fnbot.2024.1427786

Nouf A. Almujally, Adnan A. Rafique, Naif Al Mudawi, Abdulwahab Alazeb, Mohammed Alonazi, Asaad Algarni, Ahmad Jalal, Hui Liu

引用次数: 0

Abstract

IntroductionWhen it comes to interpreting visual input, intelligent systems make use of contextual scene learning, which significantly improves both resilience and context awareness. The management of enormous amounts of data is a driving force behind the growing interest in computational frameworks, particularly in the context of autonomous cars.MethodThe purpose of this study is to introduce a novel approach known as Deep Fused Networks (DFN), which improves contextual scene comprehension by merging multi-object detection and semantic analysis.ResultsTo enhance accuracy and comprehension in complex situations, DFN makes use of a combination of deep learning and fusion techniques. With a minimum gain of 6.4% in accuracy for the SUN-RGB-D dataset and 3.6% for the NYU-Dv2 dataset.DiscussionFindings demonstrate considerable enhancements in object detection and semantic analysis when compared to the methodologies that are currently being utilized.

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物体感知数据的多模式远程感知学习前沿

导言：在解释视觉输入时，智能系统会利用上下文场景学习，从而显著提高应变能力和上下文感知能力。本研究的目的是介绍一种称为深度融合网络（DFN）的新方法，该方法通过融合多物体检测和语义分析来提高上下文场景理解能力。结果为了提高复杂情况下的准确性和理解能力，DFN 结合使用了深度学习和融合技术。在 SUN-RGB-D 数据集和 NYU-Dv2 数据集上，DFN 的准确率分别提高了 6.4% 和 3.6%。

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来源期刊

Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS

CiteScore

5.20

自引率

6.50%

发文量

250

审稿时长

14 weeks

期刊介绍： Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.