FTFNet: Multispectral Image Segmentation

IF 1.6 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Justin Edwards, M. El-Sharkawy
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引用次数: 0

Abstract

Semantic segmentation is a machine learning task that is seeing increased utilization in multiple fields, from medical imagery to land demarcation and autonomous vehicles. A real-time autonomous system must be lightweight while maintaining reasonable accuracy. This research focuses on leveraging the fusion of long-wave infrared (LWIR) imagery with visual spectrum imagery to fill in the inherent performance gaps when using visual imagery alone. This approach culminated in the Fast Thermal Fusion Network (FTFNet), which shows marked improvement over the baseline architecture of the Multispectral Fusion Network (MFNet) while maintaining a low footprint.
FTFNet:多光谱图像分割
语义分割是一项机器学习任务,从医学图像到土地划分和自动驾驶汽车等多个领域的应用越来越多。实时自治系统必须是轻量级的,同时保持合理的精度。本研究的重点是利用长波红外(LWIR)图像与视觉光谱图像的融合来填补单独使用视觉图像时固有的性能差距。这种方法在快速热融合网络(FTFNet)中达到顶峰,该网络在保持低占用空间的同时,对多光谱融合网络(MFNet)的基线架构进行了显著改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Low Power Electronics and Applications
Journal of Low Power Electronics and Applications Engineering-Electrical and Electronic Engineering
CiteScore
3.60
自引率
14.30%
发文量
57
审稿时长
11 weeks
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