RDD2020: An annotated image dataset for automatic road damage detection using deep learning

Arya, Deeksha; Maeda, Hiroya; Ghosh, S. K.; Toshniwal, Durga; Sekimoto, Yoshihide

doi:10.1016/j.dib.2021.107133

Cited by 119 publications

(66 citation statements)

References 6 publications

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“…In more recent times, in [12] authors travelled across India to capture road damages on asphalted, cemented and dirt roads, acquiring about 1500 images using an iPhone 7 camera. Perhaps one of the most complete datasets for object detection is provided in [3]: it is built on pre-existing datasets and consists of approximately 26000 images, with street samples from multiple countries for further heterogeneity.…”

Section: Related Datasetsmentioning

confidence: 99%

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

Thompson¹,

Ranieri²,

Biasotti³

et al. 2022

Preprint

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This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e.: a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The methods are then evaluated on the 496 image/masks pairs in the validation set, on the 504 pairs in the test set and finally on 8 video clips. The analysis of the results is based on quantitative metrics for image segmentation and qualitative analysis of the video clips. The participation and the results show that the scenario is of great interest and that the use of RGB-D data is still challenging in this context.

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Section: Related Datasetsmentioning

confidence: 99%

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

Thompson¹,

Ranieri²,

Biasotti³

et al. 2022

Preprint

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“…It has been made freely available for supporting future research in this direction. Our data article (Arya et al 2021d) provides the complete details.…”

Section: Datamentioning

confidence: 99%

AI-Driven Road Condition Monitoring across Multiple Nations

Arya¹,

Ghosh²,

Toshniwal³

2022

AAAI

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The doctoral work summarized here is an application of Artificial Intelligence (AI) for social good. The successful implementation would contribute towards low-cost, faster monitoring of road conditions across different nations, resulting in safer roads for everyone. Additionally, the study provides recommendations for re-using the road image data and the Deep Learning models released by any country for detecting road damage in other countries.

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“…erefore, timely maintenance and repair of pavement are necessary [3]. Since the traditional arti cial detection and evaluation are slow and expensive, pavement detection and evaluation gradually transit from arti cial to intelligent [4][5][6]. An e cient and intelligent inspection system includes two parts: detection and measurement.…”

Section: Introductionmentioning

confidence: 99%

“…e 3D size measurement results directly a ect the amount of lling and the quality of pavement maintenance. With the rapid development of computer vision and sensors, many pothole detection [2,4,8] and measurement methods [5,9,10] have emerged. e maturity of pothole detection technology [2,4,11,12] and 2D pro le measurement [5,9,13,14] has been driven by large computing power and data.…”

Section: Introductionmentioning

confidence: 99%

“…With the rapid development of computer vision and sensors, many pothole detection [2,4,8] and measurement methods [5,9,10] have emerged. e maturity of pothole detection technology [2,4,11,12] and 2D pro le measurement [5,9,13,14] has been driven by large computing power and data. However, little attention has been paid to high-precision 3D measurement, especially for volume measurement.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-Precision Volume Measurement of Potholes in Pavement Maintenance

Huang

Zhou

Liu

et al. 2022

Mathematical Problems in Engineering

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Accurate three-dimensional measurement of potholes is a significant concern in road maintenance. However, the assessment of road potholes still relies heavily on human inspectors to make maintenance suggestions. To realize full-field measurement of pavement pothole automatically, a high-precision volume measurement method based on second-order Taylor expansion is proposed, where the second-order Taylor estimation of each point is converted into a convolution operation. On the one hand, this method discards the second-order fitting of the surface, which greatly reduces the computational complexity. On the contrary, the second-order Taylor estimation is not restricted by the surface shape because it only depends on the distribution of the adjacent points. Experiments on synthesized and real point sets demonstrate that the proposed method outperforms the state-of-the-art methods under various point cloud shapes.

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RDD2020: An annotated image dataset for automatic road damage detection using deep learning

Cited by 119 publications

References 6 publications

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

AI-Driven Road Condition Monitoring across Multiple Nations

High-Precision Volume Measurement of Potholes in Pavement Maintenance

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