Abstract
Quality automobile inspection is one of the critical application areas to achieve better quality at low cost and can be obtained with the advance computer vision technology. Whether for the quality inspection or the automatic assembly of automobile parts, automatic recognition of automobile parts plays an important role. In this article, vehicle parts are classified using deep neural network architecture designed based on ConvNet. The public dataset available in CompCars [1] were used to train and test a VGG16 deep learning architecture with a fully connected output layer of 8 neurons. The dataset has 20,439 RGB images of eight interior and exterior car parts taken from the front view. The dataset was first separated for training and testing purpose, and again training dataset was divided into training and validation purpose. The average accuracy of 93.75% and highest accuracy of 97.2% of individual parts recognition were obtained. The classification of car parts contributes to various applications, including car manufacturing, model verification, car inspection system, among others.
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Acknowledgement
This work was funded by Project “INDTECH 4.0 – New Technologies for smart manufacturing”, no. POCI- 01-0247-FEDER-026653, financed by the European Regional Development Fund (ERDF), through the COMPETE 2020 - Competitiveness and Internationalization Operational Program (POCI).
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Khanal, S.R., Amorim, E.V., Filipe, V. (2021). Classification of Car Parts Using Deep Neural Network. In: Gonçalves, J.A., Braz-César, M., Coelho, J.P. (eds) CONTROLO 2020. CONTROLO 2020. Lecture Notes in Electrical Engineering, vol 695. Springer, Cham. https://doi.org/10.1007/978-3-030-58653-9_56
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DOI: https://doi.org/10.1007/978-3-030-58653-9_56
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