CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks (2022-08-27T00:00:00.000000Z)

TL;DR

This paper combines previously available datasets and unifying the annotations by tackling the inherent problems within each dataset, such as noise and distortions, and presents a pipeline that combines Image Processing and Deep Learning models.

Abstract

The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. However, their scope of work is limited to detecting cracks on a single type of surface (walls, pavements, glass, etc.). The metrics used to evaluate these methods are also varied across the literature, making it challenging to compare techniques. This paper addresses these problems by combining previously available datasets and unifying the annotations by tackling the inherent problems within each dataset, such as noise and distortions. We also present a pipeline that combines Image Processing and Deep Learning models. Finally, we benchmark the results of proposed models on these metrics on our new dataset and compare them with state-of-the-art models in the literature.

Authors

Shreyas Kulkarni

1 papers

Shreyas Singh

1 papers

Dhananjay Balakrishnan

1 papers

TL;DR

Abstract

Authors

References49 items

DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack Segmentation

A critical review and comparative study on image segmentation-based techniques for pavement crack detection

Generative Adversarial Networks

Ceramic Cracks Segmentation with Deep Learning

Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

Emerging Properties in Self-Supervised Vision Transformers

Detection of concealed cracks from ground penetrating radar images based on deep learning algorithm

Surface crack detection using deep learning with shallow CNN architecture for enhanced computation

Comparison of crack segmentation using digital image correlation measurements and deep learning

Classification and quantification of cracks in concrete structures using deep learning image-based techniques

Crack Detection from a Concrete Surface Image Based on Semantic Segmentation Using Deep Learning

SDDNet: Real-Time Crack Segmentation

Concrete crack detection and quantification using deep learning and structured light

Optimized Deep Encoder-Decoder Methods for Crack Segmentation

A research on an improved Unet-based concrete crack detection algorithm

Automated Pavement Crack Segmentation Using U-Net-Based Convolutional Neural Network

Crack Detection and Segmentation Using Deep Learning with 3D Reality Mesh Model for Quantitative Assessment and Integrated Visualization

Concrete crack detection using context‐aware deep semantic segmentation network

Generative Adversarial Networks for Road Crack Image Segmentation

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

DeepCrack: A deep hierarchical feature learning architecture for crack segmentation

Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

Robust Concrete Crack Detection Using Deep Learning-Based Semantic Segmentation

Automated Road Crack Detection Using Deep Convolutional Neural Networks

SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks

Automatic road crack detection and classification using image processing techniques, machine learning and integrated models in urban areas: A novel image binarization technique

Concrete Cracks Detection Based on Deep Learning Image Classification

Pavement crack detection using Otsu thresholding for image segmentation

Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding

Road surface crack detection: Improved segmentation with pixel-based refinement

How to get pavement distress detection ready for deep learning? A systematic approach

Automatic Road Crack Detection Using Random Structured Forests

Image-to-Image Translation with Conditional Adversarial Networks

Xception: Deep Learning with Depthwise Separable Convolutions

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

Detection crack in image using Otsu method and multiple filtering in image processing techniques

Deep Residual Learning for Image Recognition

You Only Look Once: Unified, Real-Time Object Detection

U-Net: Convolutional Networks for Biomedical Image Segmentation

CrackTree: Automatic crack detection from pavement images

Crack Detection Using Fully Convolutional Network in Wall-Climbing Robot

DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation

Training a semantic segmentation model for cracks in the concrete lining of tunnel

Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

Segmentation, ch. 5 of image processing

PAVEMENT DISTRESS ANALYSIS USING IMAGE PROCESSING TECHNIQUES

Aiglern

Crackseg9k: A Collection of Crack Segmentation Datasets (2022)

Field of Study