SOTAVerified

Crack Segmentation

Crack segmentation in computer vision involves identifying and delineating cracks or fractures in various types of surfaces, such as roads, pavements, walls, or infrastructure. This task is crucial for infrastructure maintenance, as it helps in assessing the condition of structures and planning repairs.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 31–40 of 63 papers

TitleStatusHype
Unified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures—0
CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures—0
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices—0
DSCformer: A Dual-Branch Network Integrating Enhanced Dynamic Snake Convolution and SegFormer for Crack Segmentation—0
Multi-temporal crack segmentation in concrete structure using deep learning approaches—0
Deep Learning-Based Fatigue Cracks Detection in Bridge Girders using Feature Pyramid Networks—0
Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation—0
Distribution-aware Noisy-label Crack SegmentationCode0
EfficientCrackNet: A Lightweight Model for Crack Segmentation—0
Vision Mamba-based autonomous crack segmentation on concrete, asphalt, and masonry surfaces—0
Show:102550
← PrevPage 4 of 7Next →

No leaderboard results yet.