Jun 1, 2018 · Crack detection is the process of detecting the crack in the structures using any of the processing techniques. The crack detection can be made in two ways. They are Destructive Testing and Non-Destructive testing. By incorporating the visual examination and surveying tools, surface condition deficiencies are evaluated [11].

Aug 20, 2020 · An insight into a wide variety of methods used for crack testing, including magnetic particle inspection, dye penetrant inspection, ultrasound, and more. More results En

Therefore, an efficient and innovative algorithm for crack detection is proposed, which utilizes a transformer and a multilevel cross-scale weighted feature fusion module, as well as a progressive transfer learning (TL) strategy.

Jul 29, 2024 · Non-destructive testing (NDT) includes different techniques for assessing material quality characteristics like density, hardness, or thermal conductivity, without damaging them. The goal is to evaluate the assets’ durability and detect defects like cracks, inclusions, or …

Apr 16, 2025 · Deep learning-based image processing methods are commonly used for bridge crack detection. Aiming at the problem of missed detections and false positives caused by light, stains, and dense cracks ...

Feb 23, 2024 · We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using...

Apr 16, 2025 · This paper proposes an intelligent road crack detection and analysis system, based on the enhanced YOLOv8 deep learning framework. A target segmentation model has been developed through the training of 4029 images, capable of efficiently and accurately recognizing and segmenting crack regions in roads. The model also analyzes the segmented ...

Crack detection is a critical issue in structural health monitoring, driving extensive research efforts. Numerous review papers have synthesized the literature on crack detection, providing comprehensive insights into methodologies, technologies, and challenges in the field.

May 6, 2022 · This section offers an overview of the evaluated crack detection methods for the literature, based on the methods’ objectives, crack detection techniques, technique accuracy level, and dataset utilised for model’s training and testing.

3 days ago · Consumer-grade drones with embedded vision technology offer a promising approach for intelligent detection of architectural surface anomalies. However, reliance on manually designed network architectures limits their effectiveness, as these struggle to represent complex textures, reduce crack segmentation accuracy, and fail to efficiently ...

Nov 1, 2023 · To address the challenges of inefficient and multi-step detection in traditional concrete crack detection methods, a novel deep learning-based model, called YOLOv5-IDS, is proposed based on You Only Look Once network v5 (version 6.2) with the combination of bilateral segmentation network while introducing a dilated convolution, pyramid pooling m...

Using AI for crack detection and segmentation. Learn why it’s important to detect cracks in industrial settings and how crack detection using deep learning models like Ultralytics YOLOv8 automates this process.

Aug 14, 2021 · Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods...

Mar 11, 2014 · The aims of this paper are to summarize the knowledge about cracking and its sources, review both existing and emerging methods for crack detection and characterization, and identify the advantages and challenges for these methods.

Jul 13, 2020 · With the advancement of machine learning (ML) and deep learning (DL), there is a great opportunity to enhance the development of automatic crack detection algorithms.

Many nondestructive examination techniques exist for the detection of discrete cracking, as opposed to general creep damage. These are routinely applied at inspection intervals throughout the life of a component and form an indispensable part of a life assessment program.

Jul 2, 2022 · Researchers have explored automated crack detection in concrete infrastructures, emphasizing crack identification, categorization, crack length, and width measurement.

Aug 14, 2021 · Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning.

Jan 27, 2022 · In this paper, a comprehensive literature review of deep learning-based crack detection studies and the contributions they have made to the field is presented.

Jan 1, 2022 · Structural images are used for various purposes, e.g., automatic locating of cracks, classifying the cracks, and measuring crack properties to monitor the structure for proper caring.

Crack Detection Based on Infrared thermography (IRT) - PyTorch. 1. Dataset for multi crack-detection tasks. Infrared thermography and deep learning for multi crack-detection tasks (multiple-type distress detection, crack severity classification, and crack segmentation). 2. Dataset (for crack segmentation)

Nov 21, 2022 · Computer vision-based crack analysis for civil infrastructure has become popular to automatically process inspection imaging data for crack detection, localisation and quantification.

Mar 3, 2021 · There are three levels of crack detection from photos: · The image is divided into patches and each patch is assigned a crack or non-crack label. · A rectangle is drawn around any detected crack. · Each pixel is labelled as crack or non-crack.