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一个新的CNN-ViT模型与级联抽样,以实现高效的裂细分
Ahmed Tibermacine1, Imad Eddine Tibermacine2, Zineddine S Kahhoul3
1LESIA Laboratory, Department of Computer Science, University of Biskra, Biskra 07000, Algeria.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究提出了一种高效的混合人工智能模型,用于在基础设施图像中准确的裂细分. 新型架构提高了缺陷检测,同时降低了实用实时检查的计算成本.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 由于低对比度,不连续的缺陷和环境噪音,基础设施中精确的裂纹细分很难.
- 现有的变压器模型提供了全球背景,但具有很高的计算开销,限制了实际使用.
研究的目的:
- 开发一个高效的混合细分架构,以改善民用基础设施的裂检测.
- 解决现有模型在实际应用中的计算和内存效率方面的局限性.
主要方法:
- 一种混合架构,结合了卷积编码器 (本地特征) 和轻量级变压器瓶 (全球上下文).
- 一个渐进式解码器,具有多级别的跳过功能融合,以恢复空间分辨率.
- 培训包括一个复合的子-二进制交叉的目标和一个专门的代币化策略,以保护细裂纹.
主要成果:
- 在四个公共基准上,与卷积式,基于变压器和混合基线相比,性能得到了持续的改善.
- 废弃性研究证实了单个设计组件的有效性.
- 有利的运行时配置,显示低延迟和内存使用率,用于实时部署.
结论:
- 提出的高效混合模型显著提高了裂细分的准确性和效率.
- 该架构适用于资源有限的环境和实时检查平台.
- 这项工作为自动化基础设施健康监测提供了实际解决方案.
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