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.

翻译：路边广告牌及其他形式的户外广告在营销活动中扮演着关键角色；然而，它们也可能分散驾驶员注意力，潜在地导致事故发生。本研究深入探讨了从驾驶员视角拍摄的图像中路侧广告的重要性。首先，评估了神经网络在检测道路沿线广告方面的有效性，重点关注YOLOv5和Faster R-CNN模型。其次，研究探讨了利用显著性提取方法确定广告牌显著性的问题。采用UniSal和SpectralResidual方法为每幅图像生成显著性图。研究建立了一个在城市高速公路驾驶过程中采集的眼动追踪会话数据库，用以评估这些显著性模型。