Image processing-based automatic detection of asphalt pavement rutting using a novel metaheuristic optimized machine learning approach

Cao, Minh-Tu; Chang, Kuan-Tsung; Nguyen, Ngoc‐Mai; Tran, Van-Duc; Tran, Xuan-Linh; Hoang, Nhat‐Duc

doi:10.1007/s00500-021-06086-5

Cited by 35 publications

(8 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Undoubtedly, several computer vision-based approaches have demonstrated success in different fields (Ahmed and Mohammed 2023;Cao et al 2021;Mohan and Poobal 2018;Yamaguchi et al 2008), particularly in concrete infrastructures such as bridges, precast tunnels, underground pipes, and asphalt pavements (Koch et al 2015;Qu and Chang 2023). These methods primarily involve single-image processing to acquire crack shapes.…”

Section: Urnydcuinmentioning

confidence: 99%

A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer Vision

Qi,

Li,

Huang

2024

Preprint

View full text Add to dashboard Cite

Automated identification of concrete cracks is essential for ensuring infrastructure integrity and sustained safety. However, the irregularities and non-uniformity of concrete cracks present challenges in achieving a thorough, automated crack assessment meeting current specifications while reducing human involvement. Although relying on crack width to evaluate the degree of cracking has been widely accepted, the measurement of crack width is still limited by the measurement position, and the detection results often require additional processing, lacking intuitive representation. This paper proposes an innovative approach for a complete end-to-end detection system for assessing concrete crack grades, which includes a detection methodology and an evaluation framework. The detection method employs 227×227×3 detection cells trained using convolutional neural networks, each assigned distinct labels to slide and identify cracks. Simultaneously, the evaluation framework translates the concrete current specifications into computer-executable procedures and generates the quantitative evaluation of single and multiple cracks in concrete. The experimental findings indicate the models using fine-tuned AlexNet and VGG16 trained on the self-generated Crack-1000 dataset, generate a low error recognition rate of 5.2% and 2.4% respectively. The applications demonstrate the trained model and evaluation framework can accurately identify crack paths and provide a crack label that matches the actual situation. Compared with traditional methods, the method proposed in this article has superior recognition accuracy and end-to-end detection ability, eliminating the necessity of manual parameter adjustment. This innovative visual solution provides an effective method for detecting and evaluating cracks in concrete structures.

show abstract

Section: Urnydcuinmentioning

confidence: 99%

A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer Vision

Qi,

Li,

Huang

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…. (M. T. Cao et al, 2021) have summarized the methods in automatic crack detection into three groups, which are Image Acquisition Group, Image Processing Group and Image recognition group. Smart flying robot for image acquisition can work autonomously with low cost and high speed, but could be replaced by experts in the near future.…”

Section: Monitoring Of Crack Propagationmentioning

confidence: 99%

Pavement Crack Monitoring: Literature Review

Fahad¹,

Nagy²,

Guangpin³

et al. 2023

IJCE

View full text Add to dashboard Cite

Crack monitoring of pavements is an ever-evolving technology with new crack identification technologies being introduced frequently. Although older technologies consisted of physical removing the pavement section using coring, however new methods are available that are non-destructive and yield a higher performance than conventional technologies. This paper compiles various crack monitoring technologies such as wireless sensor networks, photo imaging, laser imaging, 3D road surface profile scans, acoustics wave propagation technology, embedded strain sensors and onboard vehicle sensors that majorly use an artificial intelligence algorithm to identify and categorize the cracks. The research also includes the use of convolutional neural network that can be used to analyze pavement images and such neural network can localize and classify the cracks for crack initiation and propagation stage. The research concludes with the favor of using the optical imaging technology called Syncrack which serves better performance in terms of time of prediction by 25% and accuracy by 30% when compared to other sensing technologies.

show abstract

“…Detection networks based on deep convolutional neural networks have become the most popular algorithms among researchers in the area of pavement distress detection [1][2][3][4][5][6][7][8][9][10][11][12]. With the development of deep learning theory and the improvement of computer hardware performance, the depth and breadth of detection networks have been increasing to achieve superior accuracy, along with a rapid increase in the number of parameters.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Pavement Distress Detection Using a Morphological Constraints-Based Data Augmentation Method

Dai

Sun

et al. 2023

Coatings

View full text Add to dashboard Cite

Pavement distress data in a single section usually presents a long-tailed distribution, with potholes, sealed cracks, and other distresses normally located at the tail. This distribution will seriously affect the performance and robustness of big data-driven deep learning detection models. Conventional data augmentation algorithms only expand the amount of data by image transformation and fail to enlarge the data diversity. Due to such a drawback, this paper proposes a novel two-stage pavement distress image augmentation pattern, in which a mask is generated randomly according to the geometric features of the distress in the first stage; and in the second stage, a distress-free pavement image with the fused mask is transformed into a pavement distress image. Furthermore, two convolutional networks, M-DCGAN and MDTMN, are designed to complete the generation task in two stages separately. In comparison with other generation algorithms, the quality and diversity of the generation results of proposed algorithms are better than other algorithms. In addition, distress detection tests are conducted which indicate that the expanded dataset can raise the IoU from 48.83% to 83.65% at maximum, and the augmented data by the proposed algorithm contributes more to the detection performance.

show abstract

Image processing-based automatic detection of asphalt pavement rutting using a novel metaheuristic optimized machine learning approach

Cited by 35 publications

References 68 publications

A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer Vision

A Study on End-to-End Detection Method of Concrete Crack Grade Based on Computer Vision

Pavement Crack Monitoring: Literature Review

Enhancing Pavement Distress Detection Using a Morphological Constraints-Based Data Augmentation Method

Contact Info

Product

Resources

About