Projects:2018s1-110 Future Submarine Project - Revision history

A1687420 at 14:43, 21 October 2018

2018-10-21T14:43:45Z

A1687420 at 14:42, 21 October 2018

2018-10-21T14:42:38Z

A1687420 at 14:36, 21 October 2018

2018-10-21T14:36:31Z

A1687420: /* Research Methodology */

2018-10-21T14:30:01Z

‎Research Methodology

A1687420: /* Introduction */

2018-10-21T14:24:32Z

‎Introduction

A1687420: /* Introduction */

2018-10-21T14:21:52Z

‎Introduction

A1687420: /* Introduction */

2018-10-21T14:17:11Z

‎Introduction

A1687707 at 00:14, 11 October 2018

2018-10-11T00:14:58Z

A1687707: /* Introduction */

2018-10-11T00:12:39Z

‎Introduction

A1687707 at 00:07, 11 October 2018

2018-10-11T00:07:04Z

@@ Line 86: / Line 86: @@
 #	Input the segmented image to the Hough Transform line detector outlined in section 4.1 to find the horizon using the DCT-based segmentation.
-== Target Detection System ==
+== Target Detection Method ==
 ===DCT and GMM Based Target Method===

@@ Line 31: / Line 31: @@
 == Introduction ==
-'''Aim'''
+===Aim===
 The project aimed to fulfil several goals, including:
 #	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.
@@ Line 38: / Line 38: @@
 ##	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'''
+===Motivation===
-== Literature Review ==
+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.
 == Research Methodology ==
@@ Line 58: / Line 52: @@
 == Horizon Detection Methods ==
-'''Hough Transform Method'''
+===Hough Transform Method===
 A horizon detection algorithm using the Hough Transform was implemented. The algorithm takes in a colour image but performs its processing steps only on luminance component, so the image is converted to greyscale.
@@ Line 76: / Line 70: @@
-'''DCT-based Method'''
+===DCT-based Method===
 This section discusses a target detection algorithm using the characteristics of the Discrete Courier Transform (DCT) coefficients. The initial step described in the algorithm is a horizon detection algorithm using these characteristics, before modelling the sea-surface with a Gaussian Mixture Model (GMM) for ship or object detection.
@@ Line 94: / Line 88: @@
 == Target Detection System ==
-'''...'''
+===DCT and GMM Based Target Method===
+This section will describe the primary target detection method developed in this project, which uses the DCT coefficients as obtained in the DCT-based horizon detector outlined in section 4.1.2. The approach taken in this implementation is to split the image into two components, sea and sky, using a horizon line detector. From the literature, the algorithm from uses this horizon detector to find the line between sea and sky. However, the algorithm adapted for this project will use the Hough Transform derived horizon line detector outlined in section 4.1.1 for a more accurate detection.
-'''...'''
+The target detection process will use the average of the DCT coefficients in each 8x8 blocks to segment the entire image into two components - sky and not-sky. This achieved by taking the DCT coefficients in each 8x8 block of image pixels and taking the normalized mean as in the DCT-based horizon detection algorithm (4.1.2). Similarly, a threshold value that best separates the DCT averages from sky regions to other regions is found. All blocks with a DCT average less than this threshold value can be classified as sky, while the remaining blocks are classified as not sky. This process will achieve a ‘sea’ and ‘not-sky’ segmentation of the image.
+While this is an effective way to eliminate the sky as a background from the image, the complex textures on the sea surfaces such as waves and wakes, some regions in the sea will pass through the threshold as significant foreground pixels. Thus, the DCT average threshold testing is only applied to the area of the image that resides above the horizon line, which is found using the Hough Transform horizon line detector.
-'''...'''
+For the sea region of the image, the texture-based features are extracted as three regions from each 8x8 block of DCT coefficients, as explained in the literature (section 2.2), and listed in a feature vector, X. The feature vector is used to train a Gaussian Mixture Model to represent a sea background model. Hence, the sea pixels used for the training must be a sufficient amount of pixels below the horizon line, to ensure that pixels from potential targets do not contaminate GMM training data.

@@ Line 56: / Line 56: @@
 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 ==
+== Horizon Detection Methods ==
-'''...'''
+'''Hough Transform Method'''
-'''...'''
+An outline of the developed algorithm follows:
-'''...'''
+MATLAB’s in-built functions for the Canny Edge Detector and Hough related functions were used for this implementation. As discussed in section 2.1, the Canny Edge Detector is optimal when the input image is pre-processed for smoothening, in order to remove image noise. The Canny detector used in the implementation does not incorporate this, so a Gaussian filter with standard deviation of 1.4 [5], is applied to achieve this noise reduction.
-== Conclusion ==
+Once the Hough transform plot is obtained, the houghpeaks() function is used to locate the peak of the Hough transform plot. For this peak the corresponding ρ and θ values are extracted. Compensating for the rotation, these two values can be used to plot a line, on the original image, with equation:
 '''...'''

@@ Line 52: / Line 52: @@
 == 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 ==

@@ Line 32: / Line 32: @@
 '''Aim'''
-#The project aimed to fulfil several goals, including:
+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.
+#	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
+#	Develop a system that
-##a.	At a close range to a detected ship, can recognise the ship type, estimate its range from the submarine and estimate its orientation.
+##	At a close range to a detected ship, can recognise the ship type, estimate its range from the submarine and estimate its orientation.
-##b.	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.
+##	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'''

← Older revision		Revision as of 00:07, 11 October 2018
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	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.		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 ==
		+
		+
		+	'''Motivation'''
		+
		+	'''Supervisors'''
		+
		+
		+	'''Supervisors'''
		+
		+
		+	'''Supervisors'''
		+	== Motivation ==
		+	== Motivation ==
		+	== Motivation ==
		+	== Motivation ==
		+	== Motivation ==
		+	== Motivation ==