Crack Segmentation
11 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Crack Segmentation
Most implemented papers
CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks
We also present a pipeline that combines Image Processing and Deep Learning models.
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors
Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually.
CrackFormer: Transformer Network for Fine-Grained Crack Detection
The CrackFormer is composed of novel attention modules in a SegNet-like encoder-decoder architecture.
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution
This paper proposes a method for crack segmentation on low-resolution images.
Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding
Our work proposes a weakly supervised approach that leverages a CNN classifier in a novel way to create surface crack pseudo labels.
Local Intensity Order Transformation for Robust Curvilinear Object Segmentation
This results in a representation that preserves the inherent characteristic of the curvilinear structure while being robust to contrast changes.
Learning-Based Defect Recognitions for Autonomous UAV Inspections
We have summarized the existing crack detection and segmentation datasets and established the largest existing benchmark dataset on the internet for crack detection and segmentation, which is open-sourced for the research community.
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
This paper proposes crack segmentation augmented by super resolution (SR) with deep neural networks.
Infrastructure Crack Segmentation: Boundary Guidance Method and Benchmark Dataset
Cracks provide an essential indicator of infrastructure performance degradation, and achieving high-precision pixel-level crack segmentation is an issue of concern.
Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation
Deep learning plays an important role in crack segmentation, but most work utilize off-the-shelf or improved models that have not been specifically developed for this task.