Papers with Code 아카이브
Crack Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 3
- 보존된 평가 기록
- 13
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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
On-Device Crack Segmentation for Edge Structural Health Monitoring
SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures
FlexiCrackNet: A Flexible Pipeline for Enhanced Crack Segmentation with General Features Transfered from SAM
Cracks in concrete
Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images
CrossDiff: Diffusion Probabilistic Model With Cross-conditional Encoder-Decoder for Crack Segmentation
Unified Few-shot Crack Segmentation and its Precise 3D Automatic Measurement in Concrete Structures
CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devices
DSCformer: A Dual-Branch Network Integrating Enhanced Dynamic Snake Convolution and SegFormer for Crack Segmentation
Multi-temporal crack segmentation in concrete structure using deep learning approaches
Deep Learning-Based Fatigue Cracks Detection in Bridge Girders using Feature Pyramid Networks
Topology-aware Mamba for Crack Segmentation in Structures
Benefiting from Quantum? A Comparative Study of Q-Seg, Quantum-Inspired Techniques, and U-Net for Crack Segmentation
Distribution-aware Noisy-label Crack Segmentation
CrackSegDiff: Diffusion Probability Model-based Multi-modal Crack Segmentation
IRFusionFormer: Enhancing Pavement Crack Segmentation with RGB-T Fusion and Topological-Based Loss
EfficientCrackNet: A Lightweight Model for Crack Segmentation
Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure
Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation
Mamba meets crack segmentation
Vision Mamba-based autonomous crack segmentation on concrete, asphalt, and masonry surfaces
Robust feature knowledge distillation for enhanced performance of lightweight crack segmentation models
Segmentation tool for images of cracks
Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges
CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections
A statistical method for crack detection in 3D concrete images
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration
Fine-tuning vision foundation model for crack segmentation in civil infrastructures
Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation
Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation
Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark Dataset
TrueDeep: A systematic approach of crack detection with less data
Dual flow fusion model for concrete surface crack segmentation
A Few-Shot Attention Recurrent Residual U-Net for Crack Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.