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자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.6
- 수록면
- 895 - 901 (7page)
- DOI
- 10.5302/J.ICROS.2026.26.0066
이용수
초록· 키워드
This study proposes a lightweight vision transformer (ViT) architecture for textile material classification subject to on-device computational constraints. Although ViTs effectively model global contextual relationships through self-attention, their high computational complexity - especially from multihead self-attention (MHSA) limits real-time deployment in resource-constrained environments. Given that textile images are dominated by repetitive and self-similar texture patterns, we investigate herein whether simplified attention mechanisms can achieve comparable performance with lower complexity. We design lightweight ViT variants by comparing MHSA and single-head self-attention (SHSA), incorporating a convolution-based feature-extraction (FE) block before the transformer encoder to enhance texture representation and reduce token length, and analyzing the effects of transformer depth and embedding dimension under similar computational budget conditions. Experimental results show that FE-based ViT models outperform convolutional- neural-network-based baselines at lower computational cost. In particular, the SHSA-FE model achieves classification accuracy outcomes up to 99% using only 0.23 GFLOPs while maintaining high accuracy across architectural variations. Attention map analysis further demonstrates that simplified attention produces more structurally aligned responses to repetitive texture patterns. These results suggest that for texture-dominant tasks, such as textile material classification, structurally simplified attention combined with efficient feature extraction provides a more suitable and computationally efficient alternative to conventional multihead transformer architectures for on-device applications.
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목차
- Abstract
- I. 서론
- II. 데이터 취득
- III. 신경망 구조
- IV. 실험 결과
- V. 결론
- REFERENCES
참고문헌
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