Title: Identification of similar design principles of passenger cars using deep neural networks

Authors: Fayaz Ahasan; Sebastian Schoenen; Andreas Witte

Addresses: Department of Research and Development, Control Expert GmbH, Langenfeld, NRW, 40764, Germany ' Department of Research and Development, Control Expert GmbH, Langenfeld, NRW, 40764, Germany ' Department of Research and Development, Control Expert GmbH, Langenfeld, NRW, 40764, Germany

Abstract: This paper demonstrates the idea of using deep neural networks to identify similar design principles. It is shown how a neural network originally trained to classify the images of cars to their respective brands, can also be used to identify car models following similar design principles across different brands. The proposed approach involves a comprehensive error analysis on the predictions of the deep neural network to draw inferences about design similarities of car models produced by different manufacturers. Several examples of car models following close design patterns across different brands are identified using this approach.

Keywords: deep neural networks; similar design principles; passenger cars; error analysis; car brand recognition; CNN; convolutional neural networks; similar car models; confusion matrix; design patent infringement.

DOI: 10.1504/JDR.2022.127571

Journal of Design Research, 2022 Vol.20 No.2, pp.79 - 104

Accepted: 20 Dec 2021
Published online: 09 Dec 2022 *

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