| Título |
Rock Detection in a Mars-Like Environment Using a CNN |
| Tipo |
Congreso |
| Sub-tipo |
Indefinido |
| Descripción |
11th Mexican Conference on Pattern Recognition (MCPR 2019) |
| Resumen |
In this paper we study the problem of rock detection in a Mars-like environment. We propose a convolutional neural network (CNN) to obtain a segmented image. The CNN is a modified version of the U-net architecture with a smaller number of parameters to improve the inference time. The performance of the methodology is proved in a dataset that contains several images of a Mars-like environment, achieving an F-score of 78.5%. |
| Observaciones |
doi 10.1007/978-3-030-21077-9_14 |
| Lugar |
Querétaro |
| País |
Mexico |
| No. de páginas |
149-158 |
| Vol. / Cap. |
Lecture Notes in Computer Science v. 11524 |
| Inicio |
2019-06-26 |
| Fin |
2019-06-29 |
| ISBN/ISSN |
978-3-030-21076-2 |
