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 1–10 of 63 papers

TitleStatusHype
Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations—0
On-Device Crack Segmentation for Edge Structural Health Monitoring—0
SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in StructuresCode3
FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM—0
Cracks in concrete—0
Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images—0
CrossDiff: Diffusion Probabilistic Model With Cross-conditional Encoder-Decoder for Crack Segmentation—0
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
Show:102550
← PrevPage 1 of 7Next →

No leaderboard results yet.