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English(EN) DROA-CLIPSeg: Prompt-Guided Thin Crack Segmentation in Low-Light Conditions

新AI框架增强低光照路面图像中的裂缝检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了DROA-CLIPSeg，一个用于在低光照条件下分割路面细小裂缝的新型框架。该参数高效系统通过两个轻量级模块来调整冻结的CLIPSeg视觉-语言模型：QKV低秩自适应，以增强裂缝特征的图像-文本对齐；以及领域残差正交适配器（DROA），以将主导裂缝模式与细微证据分离开来。该框架还引入了文本增强数据集CrackLingo-9K和CrackLingo-12K，以改进提示引导的分割。DROA-CLIPSeg在最少的参数调整下，展现出优于现有基线模型的性能。 AI

影响 在具有挑战性的低光照条件下，提高了路面检测的准确性和效率。

排序理由 该集群包含一篇详细介绍用于特定计算机视觉任务的新AI模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新AI框架增强低光照路面图像中的裂缝检测

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该集群包含一篇详细介绍用于特定计算机视觉任务的新AI模型的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Vinita Shukla, Partibha Jangra, Vinayak Pathak, Praful Hambarde, Amit Shukla ·

    DROA-CLIPSeg：低光照条件下提示引导的细微裂缝分割

    arXiv:2610.33635v1 Announce Type: cross Abstract: Pavement crack segmentation under low light is degraded by reduced contrast, shadows, headlight glare, joints, stains, and wet seams. Existing crack segmentation methods mostly assume good illumination. Low-light enhancement metho…

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