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English(EN) YOLO-AMC: An Improved YOLO Architecture with Attention Mechanisms for Building Crack Detection

YOLO-AMC通过注意力机制增强建筑物裂缝检测

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了YOLO-AMC，这是一种为改进建筑物裂缝检测而设计的增强型YOLO架构。该模型将GAM、Res-CBAM和SA等各种注意力机制集成到其特征融合层中，以更好地捕捉细微的裂缝特征。与YOLOv11和YOLOv8等基线模型相比，YOLO-AMC表现出优越的性能，在保持高效计算复杂度的同时实现了高mAP分数。该模型在边缘设备上的部署效率也显示出潜力，在准确性和实际应用之间取得了平衡。 AI

影响 这项研究为自动基础设施检查提供了一种更准确、更有效的方法，有可能提高安全性并降低维护成本。

排序理由 该集群描述了一篇关于改进特定任务的计算机视觉模型的新研究论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ching-Yu Tsai, Chia-Min Lin, Chih-Hsiang Yang, Yung-Che Wang, Jen-Shiun Chiang ·

    YOLO-AMC: An Improved YOLO Architecture with Attention Mechanisms for Building Crack Detection

    arXiv:2606.12958v1 Announce Type: new Abstract: Crack detection plays an important role in infrastructure inspection and Structural Health Monitoring (SHM). However, cracks typically appear as thin, low-contrast structures and are easily affected by background noise, posing chall…

  2. arXiv cs.CV TIER_1 English(EN) · Jen-Shiun Chiang ·

    YOLO-AMC: An Improved YOLO Architecture with Attention Mechanisms for Building Crack Detection

    Crack detection plays an important role in infrastructure inspection and Structural Health Monitoring (SHM). However, cracks typically appear as thin, low-contrast structures and are easily affected by background noise, posing challenges for existing object detection models. This…

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