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New AI framework enhances crack detection in low-light pavement images

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DROA-CLIPSeg, a novel framework designed for segmenting thin cracks in pavement under low-light conditions. This parameter-efficient system adapts a frozen CLIPSeg vision-language model using two lightweight modules: QKV Low-Rank Adaptation to enhance image-text alignment for crack features, and a Domain Residual Orthogonal Adapter (DROA) to separate dominant crack patterns from subtle evidence. The framework also introduces text-augmented datasets, CrackLingo-9K and CrackLingo-12K, to improve prompt-guided segmentation. DROA-CLIPSeg demonstrates superior performance compared to existing baselines with minimal parameter tuning. AI

IMPACT Improves pavement inspection accuracy and efficiency in challenging low-light conditions.

RANK_REASON The cluster contains a research paper detailing a new AI model for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New AI framework enhances crack detection in low-light pavement images

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The cluster contains a research paper detailing a new AI model for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    DROA-CLIPSeg: Prompt-Guided Thin Crack Segmentation in Low-Light Conditions

    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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