A Complete Process For Shipborne Sea-Ice Field Analysis Using Machine Vision

• Published in: IFAC-PapersOnLine
• Main Authors: Sandru, Andrei, Hyyti, Heikki, Visala, Arto, Kujala, Pentti
• Other Authors: Department of Electrical Engineering and Automation, Department of Mechanical Engineering, Autonomous Systems, Marine Technology, Aalto-yliopisto, Aalto University
• Format: Conference Object
• Language: English
• Published: Elsevier Science Publishers BV 2020
• Subjects: machine vision; Sea-ice; k-means; dynamic thresholding; IMU; sensor integration; Decision Support System (DSS); Sea ice
• Online Access: https://aaltodoc.aalto.fi/handle/123456789/107569; https://doi.org/10.1016/j.ifacol.2020.12.1458

A sensor instrumentation and an automated process are proposed for sea-ice field analysis using ship mounted machine vision cameras with the help of inertial and satellite positioning sensors. The proposed process enables automated acquisition of sea-ice concentration, floes size and distribution. The process contains pre-processing steps such as sensor calibration, distortion removal, orthorectification of image data, and data extraction steps such as sea-ice floe clustering, detection, and analysis. In addition, we improve the state of the art of floe clustering and detection, by using an enhanced version of the k-means algorithm and the blue colour channel for increased contrast in ice detection. Comparing to manual visual observations, the proposed method gives significantly more detailed and frequent data about the size and distribution of individual floes. Through our initial experiments in pack ice conditions,the proposed system has proved to be able to segment most of the individual floes and estimate their size and area. Peer reviewed