Efficacy Evaluation of You Only Learn One Representation (YOLOR) Algorithm in Detecting, Tracking, and Counting Vehicular Traffic in Real-World Scenarios, the Case of Morelia México: An Artificial Intelligence Approach

This research explores the efficacy of the YOLOR (You Only Learn One Representation) algorithm integrated with the Deep Sort algorithm for real-time vehicle detection, classification, and counting in Morelia, Mexico. The study aims to enhance traffic monitoring and management by leveraging advanced...

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Bibliographic Details
Published in:AI (Basel) 2024-09, Vol.5 (3), p.1594-1613
Main Authors: Guzmán-Torres, José A., Domínguez-Mota, Francisco J., Tinoco-Guerrero, Gerardo, García-Chiquito, Maybelin C., Tinoco-Ruíz, José G.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Cities
Classification
Computer vision
Confidence intervals
Developing countries
Driving conditions
Economic activity
Effectiveness
Efficiency
Infrastructure
LDCs
Machine learning
Monitoring
Monitoring systems
Neural networks
Performance evaluation
Real time
Representations
Roads & highways
smart cities
Sorting algorithms
Surveillance
Tracking
Traffic control
Traffic flow
Traffic management
traffic monitoring
Traffic planning
Urban planning
vehicle counting
Vehicles
YOLOR
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Summary:This research explores the efficacy of the YOLOR (You Only Learn One Representation) algorithm integrated with the Deep Sort algorithm for real-time vehicle detection, classification, and counting in Morelia, Mexico. The study aims to enhance traffic monitoring and management by leveraging advanced deep learning techniques. The methodology involves deploying the YOLOR model at six key monitoring stations, with varying confidence levels and pre-trained weights, to evaluate its performance across diverse traffic conditions. The results demonstrate that the model is effective compared to other approaches in classifying multiple vehicle types. The combination of YOLOR and Deep Sort proves effective in tracking vehicles and distinguishing between different types, providing valuable data for optimizing traffic flow and infrastructure planning. This innovative approach offers a scalable and precise solution for intelligent traffic management, setting new methodologies for urban traffic monitoring systems.
ISSN:2673-2688
2673-2688
DOI:10.3390/ai5030077