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
Kim, Heesu, Boulougouris, Evangelos ORCID: https://orcid.org/0000-0001-5730-007X and Kim, Sang-Hyun;-
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Item type: Conference or Workshop Item(Paper) ID code: 65839 Dates: DateEvent4 December 2018Published4 October 2018AcceptedSubjects: Naval Science > Naval architecture. Shipbuilding. Marine engineering
Technology > Hydraulic engineering. Ocean engineeringDepartment: Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering Depositing user: Pure Administrator Date deposited: 18 Oct 2018 14:27 Last modified: 06 Oct 2024 00:46 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/65839