Papers Crack Segmentation
“Crack Segmentation” 태그가 달린 논문 77편 · 필터 해제
Compass: Degradation-Simulated Reciprocal Learning with Lightweight Needle RWKV for Multimodal Crack Segmentation under Missing Modalities
In multimodal crack segmentation for industrial facilities, the key challenge is preventing missing modalities from degrading pixel-level performance while maintaining low computational cost. Existing methods struggle to…
Crack SegmentationTraining-Free Decoding of SAM3 Semantic Responses for Cross-Domain Infrastructure Crack Segmentation
Cross-domain infrastructure crack segmentation is challenged by variations in materials, imaging conditions, crack morphology, and background interference. Although text-promptable foundation models reduce the need for t…
Crack SegmentationRethinking Efficient Crack Segmentation with Task-Aligned Structural-Directional Modeling
Recent crack segmentation methods often follow generic semantic segmentation designs, using stronger backbones, hybrid CNN-Transformer-Mamba encoders, and auxiliary enhancement branches. Although effective, this raises w…
Semantic SegmentationCrack SegmentationSCRWKV: Ultra-Compact Structure-Calibrated Vision-RWKV for Topological Crack Segmentation
Achieving pixel-level accurate segmentation of structural cracks across diverse scenarios remains a formidable challenge. Existing methods face significant bottlenecks in balancing crack topology modeling with computatio…
Computational EfficiencyCrack SegmentationUnGAP: Uncertainty-Guided Affine Prompting for Real-Time Crack Segmentation
Real-time crack segmentation is vital for structural health monitoring but is plagued by aleatoric uncertainties arising from varying lighting, blur, and texture ambiguity. Current uncertainty-aware approaches typically …
Object SegmentationCrack SegmentationCrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation
Reliable crack detection and segmentation are vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challenge. To address this limitation, we propose a novel context-aware ge…
Crack SegmentationData AugmentationMixerCSeg: An Efficient Mixer Architecture for Crack Segmentation via Decoupled Mamba Attention
Feature encoders play a key role in pixel-level crack segmentation by shaping the representation of fine textures and thin structures. Existing CNN-, Transformer-, and Mamba-based models each capture only part of the req…
Crack SegmentationPrototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures
Crack detection is critical for concrete infrastructure safety, but real-world cracks often appear in low-light environments like tunnels and bridge undersides, degrading computer vision segmentation accuracy. Pixel-leve…
Representation LearningCrack SegmentationFew-Shot LearningMetric LearningCrackSegFlow: Controllable Flow Matching Synthesis for Generalizable Crack Segmentation with a 50K Image-Mask Benchmark
Defect segmentation is central to computer vision based inspection of infrastructure assets during both construction and operation. However, deployment remains limited due to scarce pixel-level labels and domain shift ac…
Crack SegmentationAutomated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation
This paper addresses the critical need for automated crack detection in the preservation of cultural heritage through semantic segmentation. We present a comparative study of U-Net architectures, using various convolutio…
Semantic SegmentationCrack SegmentationSUPER Decoder Block for Reconstruction-Aware U-Net Variants
Skip-connected encoder-decoder architectures (U-Net variants) are widely adopted for inverse problems but still suffer from information loss, limiting recovery of fine high-frequency details. We present Selectively Suppr…
Crack SegmentationImage DenoisingFast Measuring Pavement Crack Width by Cascading Principal Component Analysis
Accurate quantification of pavement crack width plays a pivotal role in assessing structural integrity and guiding maintenance interventions. However, achieving precise crack width measurements presents significant chall…
Computational EfficiencyCrack SegmentationSelf-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection
Pavement crack detection has long depended on costly and time-intensive pixel-level annotations, which limit its scalability for large-scale infrastructure monitoring. To overcome this barrier, this paper examines the fe…
Self-Supervised LearningCrack SegmentationLIDAR: Lightweight Adaptive Cue-Aware Fusion Vision Mamba for Multimodal Segmentation of Structural Cracks
Achieving pixel-level segmentation with low computational cost using multimodal data remains a key challenge in crack segmentation tasks. Existing methods lack the capability for adaptive perception and efficient interac…
Crack SegmentationAutomatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations
Tunnel lining crack is a crucial indicator of tunnels' safety status. Aiming to classify and segment tunnel cracks with enhanced accuracy and efficiency, this study proposes a two-step deep learning-based method. An auto…
Crack SegmentationDeep Learningimage-classificationImage ClassificationOn-Device Crack Segmentation for Edge Structural Health Monitoring
Crack segmentation can play a critical role in Structural Health Monitoring (SHM) by enabling accurate identification of crack size and location, which allows to monitor structural damages over time. However, deploying d…
Crack SegmentationSegmentationStructural Health MonitoringSCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures
Pixel-level segmentation of structural cracks across various scenarios remains a considerable challenge. Current methods encounter challenges in effectively modeling crack morphology and texture, facing challenges in bal…
Crack SegmentationMambaSegmentationFlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM
Automatic crack segmentation is a cornerstone technology for intelligent visual perception modules in road safety maintenance and structural integrity systems. Existing deep learning models and ``pre-training + fine-tuni…
Computational EfficiencyCrack SegmentationGeneral KnowledgeSegmentation+2Cracks in concrete
Finding and properly segmenting cracks in images of concrete is a challenging task. Cracks are thin and rough and being air filled do yield a very weak contrast in 3D images obtained by computed tomography. Enhancing and…
Crack SegmentationContext-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images
The accurate detection and segmentation of pavement distresses, particularly tiny and small cracks, are critical for early intervention and preventive maintenance in transportation infrastructure. Traditional manual insp…
Computational EfficiencyCrack SegmentationDecoderSegmentation