Roadside billboards represent a central element of outdoor advertising, yet their presence may contribute to driver distraction and accident risk. This study introduces a fully automated pipeline for billboard detection and driver gaze duration estimation, aiming to evaluate billboard relevance without reliance on manual annotations or eye-tracking devices. Our pipeline operates in two stages: (1) a YOLO-based object detection model trained on Mapillary Vistas and fine-tuned on BillboardLamac images achieved 94% mAP@50 in the billboard detection task (2) a classifier based on the detected bounding box positions and DINOv2 features. The proposed pipeline enables estimation of billboard driver gaze duration from individual frames. We show that our method is able to achieve 68.1% accuracy on BillboardLamac when considering individual frames. These results are further validated using images collected from Google Street View.

翻译：路边广告牌是户外广告的核心元素，但其存在可能导致驾驶员分心并增加事故风险。本研究提出了一种全自动的广告牌检测与驾驶员注视时长估计流程，旨在无需人工标注或眼动追踪设备的情况下评估广告牌的相关性。我们的流程分为两个阶段：(1) 基于YOLO的目标检测模型在Mapillary Vistas数据集上训练，并在BillboardLamac数据集上微调，在广告牌检测任务中达到94% mAP@50；(2) 基于检测边界框位置与DINOv2特征的分类器。所提出的流程能够从单帧图像中估计驾驶员对广告牌的注视时长。实验表明，在单帧图像评估中，我们的方法在BillboardLamac数据集上能达到68.1%的准确率。这些结果进一步通过从Google街景图像采集的数据得到了验证。