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Article

Intelligent Traffic Management: Comparative Evaluation of YOLOv3, YOLOv5, and YOLOv8 for Vehicle Detection in Urban Environments in Montería, Colombia

by
Darío Doria Usta
1,*,
Ricardo Hundelshaussen
1,*,
César López Martínez
1,
João Felipe Coimbra Leite Costa
2 and
Diego Machado Marques
2
1
Escuela de Ingenierías y Arquitectura, Facultad de Ingeniería Industrial, Universidad Pontificia Bolivariana, Carrera 6 No. 97 A–99, Montería 230029, Colombia
2
Departamento de Engenharia de Minas, Universidade Federal do Rio Grande do Sul, Escola de Engenharia, Av. Bento Gonçalves, Bairro Agronomia, Porto Alegre 90010-150, Brazil
*
Authors to whom correspondence should be addressed.
Future Transp. 2025, 5(4), 191; https://doi.org/10.3390/futuretransp5040191
Submission received: 5 October 2025 / Revised: 13 November 2025 / Accepted: 1 December 2025 / Published: 5 December 2025
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Abstract

This study compares the performance of three YOLO-based object detection models—YOLOv3, YOLOv5, and YOLOv8—for vehicle detection and classification at an urban intersection in Montería, Colombia. Recordings from five consecutive days, spanning three time slots, were used, totaling approximately 135,000 frames with variability in lighting and weather conditions. Frames were preprocessed by maintaining the aspect ratio and were normalized according to each model. The evaluation employed models pre-trained on COCO, without fine-tuning, enabling an objective assessment of their generalization capacity. Precision, recall, F1-score, and mAP@0.5 were computed globally and by vehicle class. YOLOv5 achieved the best balance between precision and recall (F1-score = 0.78) and the highest mAP (0.63), while YOLOv3 showed lower recall and mAP, and YOLOv8 performed competitively but slightly below YOLOv5. Cars and motorcycles were the most robust classes, whereas bicycles and trucks showed greater detection challenges. Visual evaluation confirmed stable performance on cloudy days and in light rain, with reduced accuracy under sunny conditions with high contrast. These findings highlight the potential of modern YOLO architectures for intelligent urban traffic monitoring and management. The generated dataset constitutes a replicable resource for future mobility research in similar contexts.
Keywords: YOLOv3; YOLOv5; YOLOv8; computer vision; vehicle detection; traffic congestion YOLOv3; YOLOv5; YOLOv8; computer vision; vehicle detection; traffic congestion

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MDPI and ACS Style

Doria Usta, D.; Hundelshaussen, R.; López Martínez, C.; Coimbra Leite Costa, J.F.; Machado Marques, D. Intelligent Traffic Management: Comparative Evaluation of YOLOv3, YOLOv5, and YOLOv8 for Vehicle Detection in Urban Environments in Montería, Colombia. Future Transp. 2025, 5, 191. https://doi.org/10.3390/futuretransp5040191

AMA Style

Doria Usta D, Hundelshaussen R, López Martínez C, Coimbra Leite Costa JF, Machado Marques D. Intelligent Traffic Management: Comparative Evaluation of YOLOv3, YOLOv5, and YOLOv8 for Vehicle Detection in Urban Environments in Montería, Colombia. Future Transportation. 2025; 5(4):191. https://doi.org/10.3390/futuretransp5040191

Chicago/Turabian Style

Doria Usta, Darío, Ricardo Hundelshaussen, César López Martínez, João Felipe Coimbra Leite Costa, and Diego Machado Marques. 2025. "Intelligent Traffic Management: Comparative Evaluation of YOLOv3, YOLOv5, and YOLOv8 for Vehicle Detection in Urban Environments in Montería, Colombia" Future Transportation 5, no. 4: 191. https://doi.org/10.3390/futuretransp5040191

APA Style

Doria Usta, D., Hundelshaussen, R., López Martínez, C., Coimbra Leite Costa, J. F., & Machado Marques, D. (2025). Intelligent Traffic Management: Comparative Evaluation of YOLOv3, YOLOv5, and YOLOv8 for Vehicle Detection in Urban Environments in Montería, Colombia. Future Transportation, 5(4), 191. https://doi.org/10.3390/futuretransp5040191

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