Relevant bibliographies by topics / Advertisement billboard detection

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  2. Conference papers

Academic literature on the topic 'Advertisement billboard detection'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Advertisement billboard detection"

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Romi, Fadillah Rahmat, Dennis, Salim Sitompul Opim, Purnamawati Sarah, and Budiarto Rahmat. "Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 5 (2019): 2659–66. https://doi.org/10.12928/TELKOMNIKA.v17i5.11276.

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In this paper, we propose an approach to detect and geotag advertisement billboard in real-time condition. Our approach is using AlexNet’s Deep Convolutional Neural Network (DCNN) as a pre-trained neural network with 1000 categories for image classification. To improve the performance of the pre-trained neural network, we retrain the network by adding more advertisement billboard images using inductive transfer learning approach. Then, we fine-tuned the output layer into advertisement billboard related categories. Furthermore, the detected advertisement billboard images will be geotagged
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Rahmat, Romi Fadillah, Dennis Dennis, Opim Salim Sitompul, Sarah Purnamawati, and Rahmat Budiarto. "Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 5 (2019): 2659. http://dx.doi.org/10.12928/telkomnika.v17i5.11276.

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Pourhossein, Fatemeh, and Abbas Mahmoudabadi. "Smart Urban Street Advertising Pattern Using Internet of Things Based on Environmental and Traffic Conditions." Urban Studies and Public Administration 2, no. 2 (2019): 44. http://dx.doi.org/10.22158/uspa.v2n2p44.

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<p><em>The rapid development of communications together with the growing usage of Internet of Things have recently provided an opportunity for transformation of information and created a new generation of advertising, called smart advertising. The main aim of this research work is to develop a pattern on smart urban street advertising to offer a meaningful connection between the </em><em>“</em><em>parameters received from the environment</em><em>”</em><em> and the </em><em>“</em><em>planned messages” displayed
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Adio, Aanuoluwa O., Caleb O. Akanbi, Adepeju A. Adigun, Abdulwakil Kasali, and Solagbade Adisa. "Assessing Selected CNN Models for Efficient Feature Extraction in SSD for Text Detection in Advertisement Images." Dutse Journal of Pure and Applied Sciences 10, no. 2b (2024): 164–68. http://dx.doi.org/10.4314/dujopas.v10i2b.17.

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Digital advertisement promotes goods and services using digital media and technology. These digital advertisement images contain important information on the product and services being advertised and seek to persuade potential customers to take specific actions toward contacting the advertiser. Manual extraction of information from the advertisement images is tedious and prone to errors. The literature on text detection from images, billboards, and signposts using Single-shot detection (SSD) is vast. However, the literature has not explored its performance for text detection on advertisement i
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Watve, Alok, and Shamik Sural. "Soccer video processing for the detection of advertisement billboards." Pattern Recognition Letters 29, no. 7 (2008): 994–1006. http://dx.doi.org/10.1016/j.patrec.2008.01.022.

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Om Anil Kalamkar, Om Anil Khairnar, Neel Deepak Deore, Eshwari Sunil Deshmukh, and Bharti P. Ahuja. "Advertisement on Rickshaws: Enhancing Local Business Visibility Through Strategic Outdoor Advertising." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 978–80. https://doi.org/10.47392/irjaeh.2025.0140.

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Adviksha is a geographically optimized rickshaw advertising platform improving localized business outreach through transit advertisement analytics. Unlike conventional static advertising, such as billboards, dynamic targeting, and adaptive optimization of reach can't be cost-efficient. Adviksha overcomes these limitations by means of a distributed digital architecture that combines real-time geo-location tracking with predictive ad analytics and automated campaign management powered by a scalable cloud-based infrastructure. The system architecture includes a Flutter-based frontend, sports back
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Ptak, Bartosz, and Marek Kraft. "Mapping urban large‐area advertising structures using drone imagery and deep learning‐based spatial data analysis." Transactions in GIS, July 9, 2024. http://dx.doi.org/10.1111/tgis.13208.

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AbstractThe 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 are
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Conference papers on the topic "Advertisement billboard detection"

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Liu, Huxiao, Lianhai Wang, Weinan Zhang, and Wei Wang. "An Illegal Billboard Advertisement Detection Framework Based on Machine Learning." In the 2nd International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3358528.3358549.

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Watve, A. K., and S. Sural. "Detection of on-field advertisement billboards from soccer telecasts." In IET International Conference on Visual Information Engineering (VIE 2006). IEE, 2006. http://dx.doi.org/10.1049/cp:20060494.

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Mardhekar, Amogh, Rushikesh Pawar, Rucha Mohod, Rohit Shirudkar, and Umesh N. Hivarkar. "Towards Automation of Reference Data Generation for ADAS/AD Functions Development – ALiVA Framework." In International Automotive CAE Conference – Road to Virtual World. SAE International, 2024. http://dx.doi.org/10.4271/2024-28-0022.

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<div class="section abstract"><div class="htmlview paragraph">The advancements towards autonomous driving have propelled the need for reference/ground truth data for development and validation of various functionalities. Traditional data labelling methods are time consuming, skills intensive and have many drawbacks. These challenges are addressed through ALiVA (automatic lidar, image & video annotator), a semi-automated framework assisting for event detection and generation of reference data through annotation/labelling of video & point-cloud data. ALiVA is capable
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