一种道路广告牌显著性估计方法
A method for estimating roadway billboard salience
AI总结：
本研究针对路边广告易分散驾驶员注意力的问题，结合YOLOv5、Faster R-CNN目标检测模型与UniSal、SpectralResidual显著性提取方法估计广告牌显著性，并搭建城市高速驾驶眼动数据库评估模型效果。
摘要：
路边广告牌及其他形式的户外广告在营销活动中发挥着关键作用，但它们也会分散驾驶员的注意力，有可能引发事故。本研究深入探讨了从驾驶员视角拍摄的图像中路边广告的重要性。首先，本研究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，本研究采用显著性提取方法来确定广告牌的重要性，使用UniSal（统一显著性）和SpectralResidual（光谱残差）方法为每张图像生成显著性图。本研究建立了一个在城市高速公路驾驶期间采集的眼动实验数据库，用以评估这些显著性模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
