Detecting obstacles from camera image at open sea

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master_Knébel_Miklós_2020.pdf (1.02 MB)

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Perustieteiden korkeakoulu | Master's thesis
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Date

2020-08-18

Department

Major/Subject

Autonomous Systems

Mcode

ELEC3055

Degree programme

Master's Programme in ICT Innovation

Language

en

Pages

44+10

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Abstract

While self-driving cars are a hot topic in these days, fewer people know that the same level of automation is being developed in the maritime industry. To enhance safety on board and to ensure the optimal utilization of crew members, automated assistant solutions are implemented on cargo ships and vessels. This thesis deals with a monocular camera-based system, that is capable of detection obstacles in open sea scenarios, and to estimate surrounding vehicles’ distance and bearing. After a solid research of existing methods and literature, an algorithm has been developed, containing three main parts. First, the real-world measurement data and camera images are being processed. Secondly, object detection is achieved with the YOLO deep learning methods that achieves at a high framerate and can be used for real-time applications. Lastly, distance and bearing values of detected obstacles are estimated based on geometrical calculations and mathematical equations that are validated with ground truth measurement data. Having multiple weeks of recorded measurement data from a RoPax vessel operating from Helsinki, allowed testing and validation already during the development phase. Results have shown that the systems’ detection capability is highly affected by the image resolution, and that distance estimation performance is reliable until 2-3 kilometers, but estimation errors rise at farther distances, due to physical limitations of cameras. In addition, as an interesting evaluation method, a survey has been conducted with industry professionals, to compare human distance estimation capability with the developed system. As a conclusion it can be stated that a significant need and huge potential can be found in automated safety solution in the maritime industry.

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Supervisor

Zhou, Quan

Thesis advisor

Tervo, Kalevi

Keywords

computer vision, object detection, maritime, distance estimation, deep learning

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https://urn.fi/URN:NBN:fi:aalto-202008245157

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[dipl] Perustieteiden korkeakoulu / SCI