Lightweight road manager: smartphone-based automatic determination of road damage status by deep neural network

By Hiroya Maeda, Yoshihide Sekimoto, Toshikazu Seto. 2016

35 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. M. Mohammadi
2. Ala Al-Fuqaha
3. Sameh Sorour
4. Mohsen Guizani
5. Hiroya Maeda
6. Yoshihide Sekimoto
7. Toshikazu Seto
8. Takehiro Kashiyama

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “In [152], the problem of road damage detection was addressed using DNNs that gets its data through crowd-sourcing, which can be enabled by IoT devices.”
• Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images (2018, methodology): “However, the method proposed by Maeda et al. (Maeda et al., 2016), which uses 256 × 256 pixel images, identifies the damaged road surfaces, but does not classify them into different types.”