Development of a Deep Learning-Based System for Road Traffic Anomaly Detection-Reference-Cited by-同舟云学术

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Development of a Deep Learning-Based System for Road Traffic Anomaly Detection

Published:2024-04-02 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)
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Author:

Ogundokun Roseline Oluwaseun¹,Ashiedu Nicole¹,Olaniyan Julius¹,Olaniyan Deborah¹,Awoniyi Charles²,Victoria Adedayo-Ajayi¹,Akande Hakeem Babalola³,Adebiyi Abayomi A.⁴

Affiliation:

1. Landmark Univerisity,Department of Computer Science,Omu Aran,Nigeria

2. Bupa Insurance Services,London,United Kingdom

3. University of Ilorin,Department of Telecommunication Science,Nigeria

4. Durban University of Technology,Department of Electrical Power Engineering,South Africa

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10629651/10629654/10630004.pdf?arnumber=10630004

Reference28 articles.

1. Investigation of YOLO models in the detection and classification of multiple negative road anomalies

2. Edge AI-Based Automated Detection and Classification of Road Anomalies in VANET Using Deep Learning

3. A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees

4. A Convolutional Neural Network Approach for Road Anomalies Detection in Bangladesh with Image Thresholding

5. Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

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