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http://dx.doi.org/10.7837/kosomes.2022.28.4.499

A Study on the Development of YOLO-Based Maritime Object Detection System through Geometric Interpretation of Camera Images  

Kang, Byung-Sun (Graduate School of Mokpo National Maritime University)
Jung, Chang-Hyun (Mokpo National Maritime University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.4, 2022 , pp. 499-506 More about this Journal
Abstract
For autonomous ships to be commercialized and be able to navigate in coastal water, they must be able to detect maritime obstacles. One of the most common obstacles seen in coastal area are the farm buoys. In this study, a maritime object detection system was developed that detects buoys using the YOLO algorithm and visualizes the distance and bearing between buoys and the ship through geometric interpretation of camera images. After training the maritime object detection model with 1,224 pictures of buoys, the precision of the model was 89.0%, the recall was 95.0%, and the F1-score was 92.0%. Camera calibration had been conducted to calculate the distance and bearing of an object away from the camera using the obtained image coordinates and Experiment A and B were designed to verify the performance of the maritime object detection system. As a result of verifying the performance of the maritime object detection system, it can be seen that the maritime object detection system is superior to radar in its short-distance detection capability, so that it can be used as a navigational aid along with the radar.
Keywords
Autonomous ship; Object detection; YOLO; Geometry; Camera calibration;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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