An Illegal Billboard Advertisement Detection Framework Based on Machine Learning

Liu, Huxiao; Wang, Lianhai; Zhang, Weinan; Wang, Wei

doi:10.1145/3358528.3358549

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Cited by 4 publications

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“…For example, a good number of studies have investigated the designing and modeling of outdoor billboards. Notable studies investigated the designing and modelling of outdoor billboards include but not limited to the following (Abu Hasna & Ajeeb, 2020;Suprayitno, 2016;Liu et al, 2019;Siddiqui et al, 2016). These researchers investigated the best ways of designing and modelling outdoor billboards as the means of creating awareness in different settings.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review on the Extant of Outdoor Advertisements Literature in Developing Countries

Swallehe¹,

Mwilongo²

2022

OJBM

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Section: Discussionmentioning

confidence: 99%

A Systematic Review on the Extant of Outdoor Advertisements Literature in Developing Countries

Swallehe¹,

Mwilongo²

2022

OJBM

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A novel Machine learning technique for fake smart watches advertisement detection

Zaheer

Tahir

Humayun

et al. 2022

2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

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Mapping urban large‐area advertising structures using drone imagery and deep learning‐based spatial data analysis

Ptak,

Kraft

2024

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The problem of visual pollution is a growing concern in urban areas, characterized by intrusive visual elements that can lead to overstimulation and distraction, obstructing views and causing distractions for drivers. Large‐area advertising structures, such as billboards, while being effective advertisement mediums, are significant contributors to visual pollution. Illegally placed or huge billboards can also exacerbate those issues and pose safety hazards. Therefore, there is a pressing need for effective and efficient methods to identify and manage advertising structures in urban areas. This article proposes a deep‐learning‐based system for automatically detecting billboards using consumer‐grade unmanned aerial vehicles. Thanks to the geospatial information from the drone's sensors, the position of billboards can be estimated. Side by side with the system, we share the very first dataset for billboard detection from a drone view. It contains 1361 images supplemented with spatial metadata, together with 5210 annotations.

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An Illegal Billboard Advertisement Detection Framework Based on Machine Learning

Cited by 4 publications

References 12 publications

A Systematic Review on the Extant of Outdoor Advertisements Literature in Developing Countries

A Systematic Review on the Extant of Outdoor Advertisements Literature in Developing Countries

A novel Machine learning technique for fake smart watches advertisement detection

Mapping urban large‐area advertising structures using drone imagery and deep learning‐based spatial data analysis

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