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

Suyahman Suyahman, Sunardi Sunardi, Murinto Murinto, Arfiani Nur Khusna


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


Convolutional Neural Network; Data Augmentation; Test Time Augmentation; Trademark Detection; TTA

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