RGB-Angle-Wheel: A new data augmentation method for deep learning models
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Deep learning models often rely on a diverse and well -augmented dataset for optimal performance. In this context, the methods of data augmentation are pivotal in boosting the models' ability to generalize. In this paper, we introduce a novel data augmentation method, which we call RGB-Angle-Wheel, to improve the performance of deep learning models on RGB format images. This method involves rotating each color channel at specific angles to generate new training data that is distinct from the original dataset but shares similar properties. Experimental results on the CIFAR-10, CIFAR-100,and COCO datasets have validated the efficacy of the proposed method for enhancing model performance. Specifically, certain transformations in the red (R) and blue (B) channels improve model accuracy significantly, whereas the effect on the green (G) channel remains limited. These results indicate that the careful selection of transformation parameters plays a critical role in enhancing model performance. The findings of the study indicate that the proposed method can be utilized specifically for image processing, image classification, object detection, and other deep learning applications. Experiments demonstrate that the proposed method improves the model's efficacy and generalizability.
Açıklama
Anahtar Kelimeler
Data augmentation, Deep learning, Convolutional neural network, RGB-Angle-Wheel
Kaynak
Knowledge-Based Systems
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
291
