用视觉注意力模型评估道路广告牌的吸引程度
A method for estimating roadway billboard salience
- 结合YOLOv5与Faster R-CNN检测道路广告牌位置
- 采用UniSal和SpectralResidual生成注意力图，量化广告牌显著性
- 基于真实驾驶场景眼动数据验证模型有效性，适合交通安全研究
道路广告牌等户外广告在营销中起关键作用，但可能分散驾驶员注意力，增加事故风险。本研究分析从驾驶员视角拍摄图像中广告牌的重要性。首先评估了神经网络在道路广告检测中的表现，重点使用YOLOv5和Faster R-CNN模型；其次，采用UniSal和SpectralResidual方法提取图像的显著性图，以判断广告牌的视觉吸引力。研究构建了一个在城市高速公路驾驶过程中采集的眼动追踪数据库，用于评估不同显著性模型的有效性。
原文摘要 · Abstract (English)
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.
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