Papers with Code · CC BY-SA 4.0

Crack Segmentation

10-shot image generation › Semantic Segmentation › Crack Segmentation 13 평가 기록 3 datasets

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

Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images

2302.12491 2023-02-24 이 페이지의 평가 4건

IoU_max: 0.573Average IOU: 0.552AHD95: 22.52HD95_min: 20.92

Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution

2021-07-25 이 페이지의 평가 3건

IoU_max: 0.558Average IOU: 0.558

Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges

2020-05-29 이 페이지의 평가 1건

IoU_max: 0.368Average IOU: 0.320AHD95: 130.47HD95_min: 95.16

Dual Super-Resolution Learning for Semantic Segmentation

2020-06-01 이 페이지의 평가 1건

IoU_max: 0.391Average IOU: 0.285AHD95: 148.97HD95_min: 44.23

Fully Convolutional Networks for Semantic Segmentation

1411.4038 2014-11-14 이 페이지의 평가 1건

mIoU: 0.59842

Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure

2409.02866 2024-09-04 이 페이지의 평가 1건

mIoU: 0.62982

SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

2105.15203 2021-05-31 이 페이지의 평가 1건

mIoU: 0.57969

U-Net: Convolutional Networks for Biomedical Image Segmentation

1505.04597 2015-05-18 이 페이지의 평가 1건

mIoU: 0.60333