Cosmetic grading of refurbished smartphones : A machine learning approach
Pastell, Juho (2021)
2021
Konetekniikan DI-ohjelma - Master's Programme in Mechanical Engineering
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2021-05-21
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202105064581
https://urn.fi/URN:NBN:fi:tuni-202105064581
Tiivistelmä
Essential aspect affecting the reselling price of a refurbished smartphones is its cosmetic condition. Sold devices are commonly categorized to different cosmetic grades, that are used to indicate the level of previous usage visible on the phone. The categorizing is mainly done manually, which makes the process error-prone and hard to do repeatably.
OptoFidelity SCORE is a tester designed to perform automatic grading of refurbished smartphones. This thesis aims to solve, how automatic grading could be performed based on image data captured with SCORE tester. The solution is required to be trainable with examples and no traditional logic should be used in the grading.
Solution for the problem is studied with a mixed approach. First a literature review is conducted, where existing techniques used for detecting surface defects are examined. Most suitable approach is then selected for implementation. The implemented system utilizes Canny edge detection algorithm and improved LeNet-5 convolutional neural network to detect dust, grease, and scratch type defects from the device surface. This defect information is used to construct a representation of the device, that a classifier model uses for predicting the grade.
Performance of the system is evaluated with an experimental approach. Implemented grading system is used for grading physical devices, for which the true grade is assigned by human operators. Grading is done with two classifiers: random forest and support vector machine, and the results are compared. Based on experimental results, by using a support vector machine, the system was able to correctly classify devices with over 95% in accuracy, precision and recall.
All the devices used in the experiments were black iPhone 7 devices. The grading was done based on defects found from front surface of the smartphone. Since only limited number of devices were used in the evaluation no generalizing conclusion about the performance could be made. More research and studies with broader set of devices are needed, to implement a system, that could surpass human based grading.
OptoFidelity SCORE is a tester designed to perform automatic grading of refurbished smartphones. This thesis aims to solve, how automatic grading could be performed based on image data captured with SCORE tester. The solution is required to be trainable with examples and no traditional logic should be used in the grading.
Solution for the problem is studied with a mixed approach. First a literature review is conducted, where existing techniques used for detecting surface defects are examined. Most suitable approach is then selected for implementation. The implemented system utilizes Canny edge detection algorithm and improved LeNet-5 convolutional neural network to detect dust, grease, and scratch type defects from the device surface. This defect information is used to construct a representation of the device, that a classifier model uses for predicting the grade.
Performance of the system is evaluated with an experimental approach. Implemented grading system is used for grading physical devices, for which the true grade is assigned by human operators. Grading is done with two classifiers: random forest and support vector machine, and the results are compared. Based on experimental results, by using a support vector machine, the system was able to correctly classify devices with over 95% in accuracy, precision and recall.
All the devices used in the experiments were black iPhone 7 devices. The grading was done based on defects found from front surface of the smartphone. Since only limited number of devices were used in the evaluation no generalizing conclusion about the performance could be made. More research and studies with broader set of devices are needed, to implement a system, that could surpass human based grading.