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

Tools

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.

[thumbnail of Kim-etal-WMTC-2018-Object-detection-algorithm-for-unmanned-surface-vehicle]
Preview
 Text. Filename: Kim_etal_WMTC_2018_Object_detection_algorithm_for_unmanned_surface_vehicle.pdf
Accepted Author Manuscript
Download (1MB)| Preview

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 logoORCID: https://orcid.org/0000-0001-5730-007X and Kim, Sang-Hyun;
CORE (COnnecting REpositories)