The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images
Open accessAbstract
Research on damage detection of road surfaces using image processing techniques has been actively conducted. This study makes three contributions to address road damage detection issues. First, to the best of our knowledge, for the first time, a large‐scale road damage data set is prepared, comprising 9,053 road damage images captured using a smartphone installed on a car, with 15,435 instances of road surface damage included in these road images. Next, we used state‐of‐the‐art object detection methods using convolutional neural networks to train the damage detection model with our data set, and compared the accuracy and runtime speed on both, using a GPU server and a smartphone. Finally, we demonstrate that the type of damage can be classified into eight types with high accuracy by applying the proposed object detection method. The road damage data set, our experimental results, and the developed smartphone application used in this study are publicly available ( https://github.com/sekilab/RoadDamageDetector/ ).
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Metadata
- Authors
- Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H.
- Venue
- Computer-Aided Civil and Infrastructure Engineering
- Publisher
- Elsevier BV
- Published
- 2018-06-30
- Volume
- 33
- Issue
- 12
- Pages
- 1127-1141
- ISSN
- 1093-9687 (ISSN-L)
- 1467-8667
- OpenAlex
- W3124942917
- Cited by
- 870
- References
- 47
- FWCI
- 52.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., & Omata, H. (2018). Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images. Computer-Aided Civil and Infrastructure Engineering. https://doi.org/10.1111/mice.12387
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