Zuzana Černeková

Zuzana Černeková, Zuzana Berger Haladová, Ján Špirka et al.

Outdoor advertising, such as roadside billboards, plays a significant role in marketing campaigns but can also be a distraction for drivers, potentially leading to accidents. In this study, we propose a pipeline for evaluating the significance of roadside billboards in videos captured from a driver's perspective. We have collected and annotated a new BillboardLamac dataset, comprising eight videos captured by drivers driving through a predefined path wearing eye-tracking devices. The dataset includes annotations of billboards, including 154 unique IDs and 155 thousand bounding boxes, as well as eye fixation data. We evaluate various object tracking methods in combination with a YOLOv8 detector to identify billboard advertisements with the best approach achieving 38.5 HOTA on BillboardLamac. Additionally, we train a random forest classifier to classify billboards into three classes based on the length of driver fixations achieving 75.8% test accuracy. An analysis of the trained classifier reveals that the duration of billboard visibility, its saliency, and size are the most influential features when assessing billboard significance.

Z. Cernekova, V. Sisik, F. Jafari

Artificial intelligence (AI) has seen a significant surge in popularity, particularly in its application to medicine. This study explores AI's role in diagnosing leukoencephalopathy, a small vessel disease of the brain, and a leading cause of vascular dementia and hemorrhagic strokes. We utilized a dataset of approximately 1200 patients with axial brain CT scans to train convolutional neural networks (CNNs) for binary disease classification. Addressing the challenge of varying scan dimensions due to different patient physiologies, we processed the data to a uniform size and applied three preprocessing methods to improve model accuracy. We compared four neural network architectures: ResNet50, ResNet50 3D, ConvNext, and Densenet. The ConvNext model achieved the highest accuracy of 98.5% without any preprocessing, outperforming models with 3D convolutions. To gain insights into model decision-making, we implemented Grad-CAM heatmaps, which highlighted the focus areas of the models on the scans. Our results demonstrate that AI, particularly the ConvNext architecture, can significantly enhance diagnostic accuracy for leukoencephalopathy. This study underscores AI's potential in advancing diagnostic methodologies for brain diseases and highlights the effectiveness of CNNs in medical imaging applications.

Zuzana Berger Haladova, Michal Zrubec, Zuzana Cernekova

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.