CNN Based Detectors on Planetary Environments: A Performance Evaluation

Federico Furlán, Elsa Rubio, Humberto Sossa, Víctor Ponce

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

An essential characteristic that an exploration robot must possess is to be autonomous. This is necessary because it will usually do its task in remote or hard-to-reach places. One of the primary elements of a navigation system is the information that can be acquired by the sensors of the environment in which it will operate. For this reason, an algorithm based on convolutional neural networks is proposed for the detection of rocks in environments similar to Mars. The methodology proposed here is based on the use of a Single-Shot-Detector (SSD) network architecture, which has been modified to evaluate the performance. The main contribution of this study is to provide an alternative methodology to detect rocks in planetary images because most of the previous works only focus on classification problems and used handmade feature vectors.

Original languageEnglish
Article number590371
JournalFrontiers in Neurorobotics
Volume14
DOIs
StatePublished - 30 Oct 2020

Keywords

  • convolutional neural network (CNN)
  • machine learning
  • planetary exploration
  • remote sensing
  • rock detection

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