Brand Logo Image Classification Based on Deep Convolutional Generative Adversarial Network

Brand logo image examines how a critical dimension of logo design, namely the naturalness of the logo color, influences brand design induced by the logo. However, high complexity is observed, requiring higher quality data and susceptibility to overfitting when there is insufficient training data. The implementation of the proposed system involved the utilization of a Deep Convolutional Generative Adversarial Network (DCGAN) reduce overfitting and efficiently handle noise and distortion in the logo image, which is essential in brand logo image processing. The brand logo image dataset forwarded to the preprocessing stage included pixel values, which may lead to the loss of contrast, shadow, sharpness, noise removal, and structure in some relevant images. The classification involved DCGAN, aiming to achieve high-quality logo image prediction, noise reduction, and handling of overfitting by incorporating synthetic logo images. The proposed DCGAN method achieves promising performance, including a mean Average Precision (mAP) of 52.25, Accuracy of 97.09%, Precision of 97.01 %, Recall of 98.10%, and F1-score of 97.11 %. This performance is compared to existing methods such as YOLOv3 and Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM).