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EE551 – Assignment 1 – Visual Inspection
Production Line Visual Inspection: Coca-Cola Bottling Plant
The most common use of image processing in an industrial setting is for the automated visual inspection of products leaving a production facility. Automated inspection is used to inspect everything from pharmaceutical drugs to textile production. It is estimated that the majority of products bought on supermarket shelves are inspected using automated “machine vision” based systems prior to dispatch. Why? – to avoid the cost of shipping a faulty or sub-standard item to a supermarket shelf that no-one wants to buy!
In this practical exercise, we are dealing with a bottling production line in a facility bottling Coca-Cola for the Irish domestic market. We have a set of images, taken under near constant factory lighting conditions, of the bottles as they leave the bottling line. The bottling company require a vision system to automatically identify a number of specific faults that may occur during the filling, labelling and capping stages of production so that these bottles can be intercepted prior to packaging.
Your task is to design and prototype a vision system to detect the set of fault conditions that may occur together with identifying the type of fault that has occurred. You will develop this prototype system using the software and the techniques discussed in the course.
Task Specification – Automated Visual Inspection
You are required to develop a visual inspection system that correctly identifies each of the following fault conditions that may occur in the bottling plant:
1. bottle under-filled or not filled at all
2. bottle over-filled
3. bottle has label missing
4. bottle has label but label printing has failed (i.e. label is white)
5. bottle label is not straight
6. bottle cap is missing
7. bottle is deformed (i.e. squashed) in some way
In each image we are only interested in classifying the central bottle in the image. One image is taken for each bottle leaving the production line so faults occurring in bottles at the sides will be detected separately when these particular bottles are themselves photographed central to the image. Additionally, some images may have no bottle in the centre of the image – this is not a fault, just a
EE551 – Assignment 1 – Visual Inspection
gap in the production flow stemming from a machine operating further up the line. Faults with side
bottles and missing bottles must be ignored by your system – only the seven faults above must be
reported. You can tackle each of the cases in any order you wish.
Some examples will contain more than one fault with the central bottle but these cases are rare –
identify as many as you can.
In implementing your solution consider the functionality available in the software provided.
Design is important – think about the problem, think about the solution. Then write code. Then test.
Evaluate. Re-design if necessary and re-test. Follow this cycle.
As this is only a prototype – efficiency of your approach is less important than performance.
Sample Data
The sample data provided is a set of 141 images of bottles leaving the bottling facility. They have
been additionally split into labelled sets for ease of prototype development. The images have been
captured by a high-speed image camera as they leave the facility – although no motion blur occurs
in the images, the positions of the bottles in the image varies slightly from image to image.
 The full set of (randomly ordered) images is available on Blackboard
Assignments/Assignment 1/Image zip archives/all images – randomised
 Individual test sets (for each fault case / additional condition) are available here (zip files):
Assignments/Assignment 1/Image zip archives/
As part of the evaluation of your design, the program you submit will also be tested on an
additional unseen set of images collected under the same environmental conditions from exactly the
same camera setup (type, position, etc).
The marks for this practical will be awarded as follows:
 Correct identification of each of the fault conditions (in 141 image sample set):
 bottle under-filled or not filled at all 10%
 bottle over-filled 10%
 bottle has label missing 10%
 bottle has label but label printing has failed 10%
 bottle label is not straight 10%
 bottle cap is missing 10%
 bottle is deformed 10%
including ignoring side faults, missing bottles correctly by reporting no fault
 Clear, well documented program source code 10%
 Report:
 Discussion / detail of system design and choices made 5%
 Evidence of the success of system in performing the specified task 5%
 Testing on an unseen set of images (carried out after submission) 10%
Total 100%
EE551 – Assignment 1 – Visual Inspection
You must submit the following:
 Working m script meeting the above “additional program specifications” for testing.
 Report (max. 750 words!) detailing your approach to the problem and the success of your
system in identifying each of the 7 bottling fault cases as well as handling normal, side fault
and missing bottle images. Provide any illustrative images (as many as you feel necessary)
of the intermediate results of the system you produce (overlays, results of processing stages
etc.). Summarise the success of your system in identifying each fault case.
Submit this as a PDF or a Microsoft Word document.
Make it clear in the initial comments of your source code how to run your m-script. Your
program must run on a PC with MATLAB and a copy of the image processing toolbox – No third
party tools or scripts may be installed. Your submitted program should process the all images –
randomised (all.zip) file (unzipped of course).
Plagiarism : You must not plagiarise your work. You may use program source code from the
provided examples, the software library itself or any other source BUT this usage must be
acknowledged in the comments of your submitted files. Automated software tools will be used to
detect cases of source code plagiarism in this practical exercise.
You should have been made aware of the university policy on plagiarism. Anyone unclear on this
must consult the course lecturer prior to submission of this practical.
To submit your work create a directory named by your capitalised Firstname and Surname (e.g.
KermitFrog). Place all required files in this directory. Zip this entire directory structure and submit
it to the Assignment 1 source code link on Blackboard. Please submit the report ONLY to the
Assignment 1 report link on Blackboard.
Deadline : See Blackboard for details

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