A Foveated Approach to Automated Billboard Detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

109 Downloads (Pure)


Understanding billboard visibility is vital when considering the value of each billboard to advertisers, hence the growing demand for artificial intelligence based approaches to visibility measurement. Addressing this need, this research
paper presents a comprehensive approach to billboard detection using street-view images. We have developed a robust billboard detection system by leveraging state-of-the-art object detection models, such as You Only Look Once (YOLOv8), YOLOv5, Faster-Region-based Convolutional Neural Network (RCNN) and CenterNet resulting in high model accuracy. We have introduced an innovative foveated approach, based on the human visual systems, that applies a Gaussian function to assign weights to billboards to determine which is the most significant billboard based on a combination of confidence and location with respect to the image centre. The approach demonstrates an improvement in overall accuracy of the detection process. In particular YOLOv8 experienced a high accuracy increase from 63.40 to 82.71 percent. This research provides valuable insights and practical solutions for billboard detection in real-time.
Original languageEnglish
Title of host publicationDIGITAL 2023
Subtitle of host publicationAdvances on Societal Digital Transformation
EditorsPetre Dini
PublisherInternational Academy, Research, and Industry Association
Number of pages9
ISBN (Print)978-1-68558-115-2
Publication statusPublished online - 11 Oct 2023
EventAdvances on Societal Digital Transformation DIGITAL 2023 - Portugal, Porto, Portugal
Duration: 25 Sept 202329 Sept 2023


ConferenceAdvances on Societal Digital Transformation DIGITAL 2023
Abbreviated titleDIGITAL 2023
Internet address


  • Object Detection
  • Deep Learning
  • YOLO
  • Convolutional Neural Network


Dive into the research topics of 'A Foveated Approach to Automated Billboard Detection'. Together they form a unique fingerprint.

Cite this