Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images
By Hiroya Maeda, Yoshihide Sekimoto, Toshikazu Seto, Takehiro Kashiyama, Hiroshi Omata. Comput. Aided Civ. Infrastructure Eng., 2018
827 citations recorded by the source corpus.
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/).
Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/04c49b649b8f4336c1f38f69b8381249c81fbf0d
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In Civil and Structural Engineering
Citations in the corpus
- Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review (2020, background): “More innovative and useful ideas for monitoring tasks, for instance to detect efflorescence and spalling [56, 74]; bolts loosening [139], rutting of asphalt pavements and potholes [79], typology of cracks, their length and width [134] have been explored.”
- RDD2022: A multi‐national image dataset for automatic road damage detection (2022, neutral): “Firstly, the dataset RDD2018 [3] was introduced in 2018, comprising 9,053 road images with 15,435 road damage instances.”