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Abstract For the safe operation of autonomous ships, a system that automatically detects the ship's surroundings is required. AIS (Automatic Identification System), RADAR (RAdio Detection And Ranging), and LIDAR (LIght Detection And Ranging) are typical equipment for detecting the ship's surroundings. However, those sensors have limits. AIS cannot detect a ship without AIS equipment. RADAR has a slow update rate, and a blind zone is created depending on the installation location. LIDAR has a short detection range. Therefore, it is necessary to use a camera to supplement existing equipment.
In this study, an image-based object recognition technology was developed. Object recognition technology was divided into three categories: detection, localization, and tracking. Detection is a technique that determines the existence and location of objects on the image. Recently, with the rapid development of deep learning, especially Convolutional Neural Network (CNN), deep learning-based object detection technology made remarkable progress. To use the detection model based on deep learning, training data is required. This study compared the open-source data set Singapore Maritime Dataset (SMD) and virtual image data set (Unity Dataset). Localization is a technique that converts the detected bounding box on the image into spatial coordinates through a camera model. Horizontal line information on the image is required. In this study, a gyro sensor was used to detect the horizontal line. Finally, tracking is the task of estimating the position and speed of objects over time. To set the Kalman filter's initial value, the target ship's headings were identified as left/right using deep learning and set as the initial value. The results of tracking target ships were compared with the AIS data to evaluate the tracking accuracy.
Publication Date 2021-02-26

Won-Jae Lee, "Image-based Object Detection and Tracking Method for the Awareness around the Ship", M.Sc. Thesis, Seoul National University, 2021.02.26

이원재, "선박 주변 인지를 위한 영상 기반 장애물 탐지 및 추적 방법", 석사학위논문, 서울대학교, 2021.02.26


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