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4 results for “Crack classification”
CRACK-CH: A Crack detection and classification dataset on complex stone masonry surfaces
<p><strong>Description</strong></p> <p>This dataset includes various images with cracks from the test sites of HYPERION H2020 project (Grant Agreement No. 821054). Specifically, square image patches of 224x224 pixels from the test sites of Naillac and St. Nikolaos Fort are included.</p> <p>The Saint Nikolaos Fort is an important part of the great fortifications of the Medieval City of Rhodes located at the entrance of the Mantraki port. At this location, there was just a chapel dedicated to Saint Nikolaos until 1464, when it was turned to a fortification. Since then it has undergone reinforcements and expansions in order to defend the city. The outer walls were built in 1480 AD and in 1863 AD it was finally transformed to a lighthouse. The second study area is the Naillac at Saint Paul’s rampart where a monumental tower was located as part of the fortification of the Commercial Harbour of Rhodes. It was constructed around 1400 AD on the Hellenistic Pier, but it was destroyed in 1863 after a severe earthquake. In 2017, the Naillac Tower was graphically reconstructed and presented as it stood until 1863, during the Ottoman rule. The Rodini Roman Bridge is one of the few ancient bridges surviving in Greece and part of the Hellenistic fortification of the city, making it a monument of great importance. It was built across the stream of Rhodini, situated outside the Medieval City and has two arched openings. The Roman Bridge is in continuous use until today and its static efficiency has deteriorated, while the scaffoldings which now support the arches are gradually rusting and losing their efficiency. Those images were split into two separate categories, facilitating the later training and evaluation of the model: “Cracks” and “No cracks”.</p> <p>The dataset is used to train and evaluate the CNN models for crack detection on complex stone masonry surfaces.</p> <p><strong>Publication</strong></p> <p>The paper is availbale here: https://arxiv.org/abs/2303.17989</p> <p><strong>If you use this dataset please cite it as CRACK-CH [reference]</strong>.<br> [Reference] Agrafiotis, Panagiotis, Doulamis, Anastasios, and Georgopoulos, Andreas. (2023) "Unsupervised crack detection on complex stone masonry surfaces", <em>arXiv preprint arXiv:2303.17989</em><br> <br> Bibtex entry:</p> <p>@misc{agrafiotis2023unsupervised,<br> title={Unsupervised crack detection on complex stone masonry surfaces}, <br> author={Panagiotis Agrafiotis and Anastastios Doulamis and Andreas Georgopoulos},<br> year={2023},<br> eprint={2303.17989},<br> archivePrefix={arXiv},<br> primaryClass={cs.CV}<br> }</p>
NCCD-PF - A pre-failure narrow concrete cracks dataset for engineering structures damage classification and semantic segmentation
<p>The NCCD-PF dataset was developed for the classification and semantic segmentation of narrow concrete cracks in engineering structures elements at the pre-failure state. It only includes cracks whose width is narrower than 0.3 mm, i.e. the limit value specified in EC 1992-1-1 for typical elements of engineering structures and environmental conditions.</p> <p>This dataset is dedicated to the early crack detection at a stage when the serviceability limit state has not yet been exceeded and the failure of a structural element has not occurred. By implementing the early crack detection approach, it is possible to protect cracks in order to stop or slow down their propagation and thus to extend the structure's lifespan.</p> <p>This dataset contains images of cracks appearing on various elements of engineering structures (bridges, viaducts, tunnels) made of reinforced concrete (including abutments, tunnel walls, concrete barriers, pillars). The images were captured on construction sites and during inspections of engineering structures, at different stages of the reinforced concrete structure's working conditions - from the construction stage (when the elements are loaded only by their own weight) to the structure's use stage (when the elements are loaded by most of the design loads). The images are also differentiated by the cause of the cracking (ex., thermal and shrinkage stresses in young concrete, excessive stresses). The images were acquired using fixed-focus cameras without prior conditioning in order to represent the real working conditions of a bridge engineer during structural inspections. The images are characterised by a high degree of complexity due to the quality of the concrete surface finish (e.g. presence of formwork marks, concrete trowel marks), which could potentially be recognised as cracks.</p> <p>This dataset is dedicated to researchers working in the fields of computer vision, machine learning and deep learning. In particular, it contains domain knowledge in structural health monitoring, so that it can support the work of engineers in detecting cracks of concrete elements in a pre-failure state.</p> <p>A detailed description of the dataset is presented in <a href="https://www.nature.com/articles/s41597-023-02839-z" target="_blank" rel="noopener">A pre-failure narrow concrete cracks dataset for engineering structures damage classification and segmentation</a> (DOI: 10.1038/s41597-023-02839-z).</p>
Dataset for: Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Asphalt pavement fatigue crack severity classification by infrared thermography and deep learning." Automation in Construction 143 (2022): 104575. https://doi.org/10.1016/j.autcon.2022.104575.</p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
Dataset for: Deep learning and infrared thermography for asphalt pavement crack severity classification
<p>This is the dataset for the following paper: </p> <p>Fangyu Liu, Jian Liu, and Linbing Wang. "Deep learning and infrared thermography for asphalt pavement crack severity classification." Automation in Construction 140 (2022): 104383. https://doi.org/10.1016/j.autcon.2022.104383. </p> <p>Data component:</p> <ul> <li>01-Visible images: this folder includes fully visible images</li> <li>02-Infrared images: this folder includes fully infrared images</li> <li>03-Fusion(50IRT) images: this folder includes fusion images (50% infrared + 50% visible)</li> <li>04-Ground truth: this folder includes ground truth (txt files): <ul> <li>00-Label_meaning.txt: the meaning of label number</li> <li>01-All_label.txt: Image, label (severity level)</li> <li>02-Train_label.txt: the training set: Image, Label (severity level)</li> <li>02-Test_label.txt: the test set: Image, Label (severity level)</li> </ul> </li> </ul>
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