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Deep Learning for Billboard Classification

Authors:
Sayali Avinash Chavan
Dermot Kerr
Sonya Coleman
Hussein Khader

Keywords: Classification; CNN-Architecture; Billboard; Image-Processing; CIFAR10.

Abstract:
Advertising is essential to increase product awareness and foster a positive outlook, which in turn helps sales. To promote the brand and its products, billboard advertisements are widely used. This paper presents a novel approach for classifying billboards. The proposed method utilises Convolutional Neural Network (CNN) architectures to extract features from the images to enable classification. The model is trained on a dataset of billboards collected from various locations and achieves results that demonstrate high classification accuracy. The system is trained and evaluated using the CIFAR10 dataset, which includes 10 classes of objects and an additional 11th class - 'billboard', is included. The experiment utilises five different CNN architectures: Basic CNN, ResNet, Visual Geometry Group (VGG), MobileNet, and DenseNet. The performance and evaluation of each architecture are presented in detail, and extensive experiments and comparisons are conducted to determine the most effective model for classifying billboards. The results indicate that a CNN and its architectural designs are a promising solution for automating the classification of billboards in the wild.

Pages: 29 to 35

Copyright: Copyright (c) IARIA, 2023

Publication date: September 25, 2023

Published in: conference

ISBN: 978-1-68558-115-2

Location: Porto, Portugal

Dates: from September 25, 2023 to September 29, 2023