Projects:2018s1-110 Future Submarine Project

Project Team

Students

Tharidu Maliduwa Arachchige

Jacob Parker

Supervisors

Dr. Danny Gibbins

Igor Dzeba (SAAB)

Abstract

This project involves the research into and development of a Contact Detection method to be used with a Submarine Optronics System. The aims of this project include:

1. Detecting the presence of a ship at long range, and if possible estimate the range and aspect of the ship
2. At closer range, the ship or target should be detected, the type of ship must be identified/verified and its range and aspect/orientation information should be determined.
3. Certain challenges must be overcome, such as:
   1. Accumulating a sufficient database storing ship models, for the large number of different ship types that would need to be recognised by the system.
   2. Limited viewing condition as:
      1. The submarine's periscope often only reach a height of 60cm above the surface of the water
      2. Tall waves and curvature of the Earth may mean that the ships are only partially visible
      3. Bad weather conditions means that a clear horizon line may not be visible, and ships/targets blend in with the backgrounds
   3. There may be non-significant anomalies in the obtained image that are not ships/targets, and should be ignored by the system (e.g. landmasses, birds/sea-ainmals on the surface, infrastructures on the surface of the water).

The project will involve thorough literature review for object detection methods using image processing techniques in maritime applications, software development and testing. This is an industry sponsored project, sponsored by SAAB Australia.

Introduction

Aim The project aimed to fulfil several goals, including:

1. Research and review existing image processing techniques on the topic of object and/or ship detection and recognition through optical images. An understanding of existing techniques is important to determine whether or not and how they can be incorporated into any designs determined for the output of the project.
2. Develop a system that
   1. At a close range to a detected ship, can recognise the ship type, estimate its range from the submarine and estimate its orientation.
   2. At long range to a detected ship, provide guidance as to what the ship might be (type verification) and provide an estimate of its range and orientation.

Motivation

1. The project was sponsored by Saab Australia, who have a major influence in the Australian Defence industry. With the upcoming submarine research and development plans, Saab Australia is highly motivated in conducting research into different technologies that may be comprised in a submarine.

For more than a century, submarine operators relied solely on direct-view optical periscopes to gain an insight to the environment above the surface of the water [1]. A long part of this time consisted of operators relied on black and white images of ship silhouettes to identify any vessels that are viewed through the periscope. However, with the evolution of technology, electronic periscopes has been developed, providing many forms of assistance to the operators in many ways. The motivation for the research conducted in this project is to develop and test image processing techniques for ship and threat detection through the optical images that can be obtained through an electronic periscope of a submarine, providing guidance to the operators in identifying the environment around them.

Literature Review

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Research Methodology

Three horizon detection methods and two target detection methods were researched and implemented as a result of this research methodology. Of which, two horizon detection methods and one target detection method with possible enhancements are discussed in this thesis. An overview and some results of the methods investigated by the project partner as a comparison to the methods described in detail in this document. Those approaches can be found in the thesis document produced by the project partner, which is listed in the references.

For testing the produced algorithms, a large data set of imagery was required. This was acquired preliminarily by using imagery available from the internet. An issue with this method of data collection lied in the fact that the sources of the imagery were inconsistent, resulting in an inconsistency in the quality and the level of relevance of the imagery to the application. Thus, further imagery was obtained first hand by recording footage of a ship dock, where there were plenty of movement of different ships arriving and departing the docks. Single frames were then extracted from the capture to test the algorithms.

Results

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Conclusion

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