Data Augmentation Using Test-Time Augmentation on Convolutional Neural Network-Based Brand Logo Trademark Detection

Suyahman, Suyahman and Sunardi, Sunardi and Murinto, Murinto and Nur Khusna, Arfiani (2024) Data Augmentation Using Test-Time Augmentation on Convolutional Neural Network-Based Brand Logo Trademark Detection. [Artikel Dosen]

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Abstract

The detection and acknowledgment of logos holds significant
importance in the corporate sphere, facilitating the detection of unauthorized logo usage and ensuring trademark uniqueness within specific industry sectors. Presently, convolutional neural networks powered by deep learning are widely utilized for image recognition.
However, their effectiveness is dependent on a substantial volume of training images which may not always be readily available. This study suggests employing Test Time Augmentation to address dataset constraints by expanding the original dataset, thereby enhancing classification accuracy and preventing overfitting. Test-Time Augmentation is a method used to improve the accuracy of convolutional neural networks by creating numerous augmented variations of the test images and then merging their predictions. The
research findings indicate that the application of TTA has the highest performance on the VGG16 model with 98% precision, 99% recall, and 98% F1-score, and 98.87% accuracy.

Item Type:	Artikel Dosen
Subjects:	T Technology > T Technology (General)
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Informatics Engineering (S1-Teknik Informatika)
Depositing User:	murinto murinto
Date Deposited:	26 Aug 2025 06:15
Last Modified:	26 Aug 2025 06:15
URI:	http://eprints.uad.ac.id/id/eprint/86532

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