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A proposed cascade fusion R-CNN network that integrates infrared and ultrasonic images achieves 99.3% accuracy and 90.4% mean average precision in identifying the type, location, and depth of damage in composite materials, outperforming standard detection models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

August 31, 2021Applied Optics

Infrared and ultrasonic intelligent damage recognition of composite materials based on deep learning

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CLCaizhi LiAir Force Engineering University XNXiangfan NieAir Force Engineering University ZCZhihao ChangXidian University

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Li et al. (2021) studied this question.

synapsesocial.com/papers/6aa9fcabf8c08cbefbf6b2ed https://doi.org/10.1364/ao.431035

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August 31, 2021Applied Optics

Infrared and ultrasonic intelligent damage recognition of composite materials based on deep learning

View Full Paper

Ask AI

Bookmark

Authors

CLCaizhi LiAir Force Engineering University XNXiangfan NieAir Force Engineering University ZCZhihao ChangXidian University

Discussion

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Li et al. (2021) studied this question.

synapsesocial.com/papers/6aa9fcabf8c08cbefbf6b2ed https://doi.org/10.1364/ao.431035

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