Early crack detection using modified spectral clustering method assisted with FE analysis for distress anticipation in cement-based composites

Vedrtnam, Ajitanshu; Kumar, Santosh; Barluenga, Gonzalo; Chaturvedi, S. C.

doi:10.1038/s41598-021-99010-8

Cited by 5 publications

(6 citation statements)

References 57 publications

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“…This technological progress facilitates automated feature analysis, surpassing traditional, error-prone methods like manual inspection. However, most research [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ] has focused more on crack detection and classification, often neglecting the detailed analysis and measurement of specific crack characteristics [ 41 , 42 , 43 , 44 ]. Study [ 45 ] used laser-scanned range images to classify roadway cracks with a deep convolutional neural network (DCNN).…”

Section: Related Workmentioning

confidence: 99%

“…Utilizing the SVM algorithm for data classification, this UAV overcomes the challenges of real-time crack detection [ 9 , 10 , 14 , 15 , 17 , 18 , 19 ] within budget constraints. While the method’s computational efficiency is a major advantage for low-cost UAVs, the paper does not address how this efficiency might impact the accuracy or reliability of the inspection results [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Related Workmentioning

confidence: 99%

“…Study [ 19 ] developed Panthera, a robotic platform for road crack segmentation and garbage detection. However, their study does not address the limitations of using the mobile mapping system (MMS) for geotagging defects, nor does it explore the technique’s scalability across larger or varied pavement types [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”

Section: Related Workmentioning

confidence: 99%

“…tion. However, their study does not address the limitations of using the mobile map system (MMS) for geotagging defects, nor does it explore the technique's scalability a larger or varied pavement types [40][41][42][43][44][45][46][47].…”

Section: Dataset Detailsmentioning

confidence: 99%

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Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

Meftah,

Hu,

Asham

et al. 2024

Sensors

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Detecting road cracks is essential for inspecting and assessing the integrity of concrete pavement structures. Traditional image-based methods often require complex preprocessing to extract crack features, making them challenging when dealing with noisy concrete surfaces in diverse real-world scenarios, such as autonomous vehicle road detection. This study introduces an image-based crack detection approach that combines a Random Forest machine learning classifier with a deep convolutional neural network (CNN) to address these challenges. Three state-of-the-art models, namely MobileNet, InceptionV3, and Xception, were employed and trained using a dataset of 30,000 images to build an effective CNN. A systematic comparison of validation accuracy across various base learning rates identified a base learning rate of 0.001 as optimal, achieving a maximum validation accuracy of 99.97%. This optimal learning rate was then applied in the subsequent testing phase. The robustness and flexibility of the trained models were evaluated using 6,000 test photos, each with a resolution of 224 × 224 pixels, which were not part of the training or validation sets. The outstanding results, boasting a remarkable 99.95% accuracy, 99.95% precision, 99.94% recall, and a matching 99.94% F1 Score, unequivocally affirm the efficacy of the proposed technique in precisely identifying road fractures in photographs taken on real concrete surfaces.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Dataset Detailsmentioning

confidence: 99%

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Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning

Meftah,

Hu,

Asham

et al. 2024

Sensors

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“…But these methods can only extract local patterns instead of global patterns, which pulls the detection results backward. Some research [8][9][10] has used model-based, traditional CV algorithms, which use geometric characteristics of images to perform crack detection globally. The advantages of modelbased techniques over feature-based techniques are that model-based techniques can detect cracks in adverse conditions such as noisy environments, poor illumination conditions, and shadow problems.…”

Section: Introductionmentioning

confidence: 99%

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

et al. 2023

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Crack inspection is important to monitor the structural health of pavement structures and make the repair process easier. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. To solve the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates the pavement and collects visual images from the surface. A deep-learning-based semantic segmentation framework named RCDNet was proposed. The RCDNet was implemented on the onboard computer of the robot to identify cracks from the visual images. The encoder-decoder architecture was utilized as the base framework of the proposed RCDNet. The RCDNet comprises a dual-channel encoder and a decoder module. The encoder and decoder parts contain a context-embedded channel attention (CECA) module and a global attention module (GAM), respectively. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested in both indoor and outdoor environments. The robot was observed to complete the inspection of a 3 m × 2 m grid within 10 min and a 2.5 m × 1 m grid within 6 min. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map was generated using the visual image results. This map highlights areas that require greater attention for repair in the test grid.

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