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Computer-vision based detection of test tubes in Corona mass test stations
To further automate the process of Coronavirus mass testing, the position of sample tubes in collecting racks has to be identified. For this, a simple computer vision approach should be realized end-to-end, including hard- and software. The approach can be evaluated in actual corona testing stations
To enable early identification of individuals infected with the coronavirus, ETH is setting up mass testing station for PCR saliva tests. Handout and collection of test kits is automated by using two machines: A modified selecta machine that contains test kits as well as a collection machine that receives test samples. The deposited samples are collected from the machine and analysed in batches. Currently, two test stations are operating; one at ETH Centre, one at ETH Campus Hönggerberg. As part of the CovMass initiative, the test stations were developed by pdz’s Feasibility Lab.
To enable early identification of individuals infected with the coronavirus, ETH is setting up mass testing station for PCR saliva tests. Handout and collection of test kits is automated by using two machines: A modified selecta machine that contains test kits as well as a collection machine that receives test samples. The deposited samples are collected from the machine and analysed in batches. Currently, two test stations are operating; one at ETH Centre, one at ETH Campus Hönggerberg. As part of the CovMass initiative, the test stations were developed by pdz’s Feasibility Lab.
For deposition of the samples, the saliva tubes are placed in a collection rack inside the collection machine. Currently, it is not tracked at which position a tube is placed. Task of the thesis is to realize a computer-vision based approach to detect the rack position at which a test tube is placed. The rack position should then be connected to the test sample. This information should be stored and communicated to the lab personal conducting the PCR test, allowing further automation of the testing process.
The hardware running the computer-vision approach should be integrated into the sample collection station and should be highly integrated. Therefore, the application should be realized using either a microcontroller or a Raspberry Pi, including a camera module.
For deposition of the samples, the saliva tubes are placed in a collection rack inside the collection machine. Currently, it is not tracked at which position a tube is placed. Task of the thesis is to realize a computer-vision based approach to detect the rack position at which a test tube is placed. The rack position should then be connected to the test sample. This information should be stored and communicated to the lab personal conducting the PCR test, allowing further automation of the testing process. The hardware running the computer-vision approach should be integrated into the sample collection station and should be highly integrated. Therefore, the application should be realized using either a microcontroller or a Raspberry Pi, including a camera module.
You are highly interested in realizing computer-vision applications from end to end, including hardware and software Desired are interest and skills in programming, especially regarding computer-vision (Python preferred) Experience in prototyping with single board computers (RasPis) or microcontrollers will help You work independently and are eager to interact with a team You approach problems systematically and manage your projects methodically You are a team player, curious and come up with innovative ideas You are willing to integrate into a professional work environment
We focus on human-centred product development and regard the link between research and education as the key to excellence in training. We see ourselves as a partner for industry and promote the continuous transfer of knowledge through cooperation, as well as the training and further education of students and graduates to strengthen the competitiveness of mechanical engineering industry.
We focus on human-centred product development and regard the link between research and education as the key to excellence in training. We see ourselves as a partner for industry and promote the continuous transfer of knowledge through cooperation, as well as the training and further education of students and graduates to strengthen the competitiveness of mechanical engineering industry.
Not specified
Stephan Fox (pd|z)
Jonas Conrad (pd|z)
LEE O 208
sfoxj@ethz.ch
Tel.: +41 (0)44 632 71 97
pdz.ethz.ch
Stephan Fox (pd|z) Jonas Conrad (pd|z) LEE O 208 sfoxj@ethz.ch Tel.: +41 (0)44 632 71 97 pdz.ethz.ch