Revolutionizing Signage Analysis: Leveraging YOLOv7 Object Detection for Comprehensive Classification and Assessment of Diverse Signage Types-Reference-Cited by-同舟云学术

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Revolutionizing Signage Analysis: Leveraging YOLOv7 Object Detection for Comprehensive Classification and Assessment of Diverse Signage Types

Published:2024-04-26 Issue: Volume: Page:111-116
ISSN:
Container-title:Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence
language:
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Author:

Tomas John Paul Quilingking¹^ORCID,Buenaventura Adam Lee B.¹^ORCID,Cruzate Jester D.¹^ORCID,Patarata Ghasutt Joshua R.¹^ORCID,Villanueva Izak Kyle E.¹^ORCID

Affiliation:

1. Mapua University, Philippines

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3669754.3669771

Reference15 articles.

1. Michael Allen. 2021. Could microscale concave interfaces help self-driving cars read road signs? Physics World. Retrieved from https://physicsworld.com/a/could-microscale-concave-interfaces-help-self-driving-cars-read-road-signs/

2. Dominique Marlowe Brucal, Alyssa Louise Canuto, and Camille Ann Garcia. 2013. A study on the design of the Philippine regulatory road signs based on drivers characteristics ergonomic designs based on drivers characteristics, ergonomic design principles and comprehension. (2013). Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/11008/

3. Road Feature Detection for Advance Driver Assistance System Using Deep Learning

4. Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips

5. Road Signs Recognition by Using YOLOv8 Model

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