Classification of damage types in mobile device screens: Using lightweight convolutional neural networks to detect cracks and scratches
Parkkinen, Ville (2020)
2020
Teknis-luonnontieteellinen DI-tutkinto-ohjelma - Degree Programme in Science and Engineering, MSc (Tech)
Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences
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Hyväksymispäivämäärä
2020-05-08
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202004273915
https://urn.fi/URN:NBN:fi:tuni-202004273915
Tiivistelmä
Evaluating the condition of used mobile devices is important part of the process of reselling and recycling smart phones and tablets. Damages on the device screen are usually identified and their severity is classified manually by visual inspection. This can lead to inconsistent and biased results. In this thesis an automated method utilizing a convolutional neural network is proposed to automate this task.
In order to make the neural network classifier usable in practical applications, it must be fast enough to perform within reasonable time even in devices with limited computational resources. The high classification accuracy of convolutional neural networks comes with high computational cost. Lightweight convolutional neural network architectures have been designed to achieve reasonable accuracy with fast inference times.
In this work popular lightweight neural network architectures are described and fine-tuned to classify damages on mobile device screens. Several methods for optimizing the accuracy and inference time are also experimented with. The most accurate network trained in this work classifies damages on mobile device screen with 84.8% accuracy in 3 seconds.
In order to make the neural network classifier usable in practical applications, it must be fast enough to perform within reasonable time even in devices with limited computational resources. The high classification accuracy of convolutional neural networks comes with high computational cost. Lightweight convolutional neural network architectures have been designed to achieve reasonable accuracy with fast inference times.
In this work popular lightweight neural network architectures are described and fine-tuned to classify damages on mobile device screens. Several methods for optimizing the accuracy and inference time are also experimented with. The most accurate network trained in this work classifies damages on mobile device screen with 84.8% accuracy in 3 seconds.