A method for estimating roadway billboard salience
Journal:
arXiv
Published Date:
Jan 13, 2025
Abstract
Roadside billboards and other forms of outdoor advertising play a crucial
role in marketing initiatives; however, they can also distract drivers,
potentially contributing to accidents. This study delves into the significance
of roadside advertising in images captured from a driver's perspective.
Firstly, it evaluates the effectiveness of neural networks in detecting
advertising along roads, focusing on the YOLOv5 and Faster R-CNN models.
Secondly, the study addresses the determination of billboard significance using
methods for saliency extraction. The UniSal and SpectralResidual methods were
employed to create saliency maps for each image. The study establishes a
database of eye tracking sessions captured during city highway driving to
assess the saliency models.
