Object detection algorithm for unmanned surface vehicle using faster R-CNN

Kim, Heesu and Boulougouris, Evangelos and Kim, Sang-Hyun (2018) Object detection algorithm for unmanned surface vehicle using faster R-CNN. In: World Maritime Technology Conference 2018, 2018-12-04 - 2018-12-07, Renaissance Shanghai Zhongshan, Part Hotel Shanghai.

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Abstract

The purpose of this research is development of vision-based object detection algorithm that recognizes a marine object, localizes the object on captured frames, and estimates the distance to the object. Faster R-CNN and stereo vision based depth estimation are combined for real-time marine object detection. The performance of this algorithm is verified by model ship detection test in towing tank. The test results showed that this algorithm is potentially applicable to real USV.

ORCID iDs

Boulougouris, Evangelos ORCID: https://orcid.org/0000-0001-5730-007X

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• Item metadata

  Item type:	Conference or Workshop Item(Paper)
  ID code:	65839
  Dates:	Date Event 4 December 2018 Published 4 October 2018 Accepted
  Subjects:	Naval Science > Naval architecture. Shipbuilding. Marine engineering Technology > Hydraulic engineering. Ocean engineering
  Department:	Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering
  Depositing user:	Pure Administrator
  Date deposited:	18 Oct 2018 14:27
  Last modified:	20 Nov 2024 01:44
  Related URLs:	Event
  URI:	https://strathprints.strath.ac.uk/id/eprint/65839