Home > Published Issues > 2024 > Volume 12, No. 2, 2024 >
JOIG 2024 Vol.12(2): 127-136
doi: 10.18178/joig.12.2.127-136

YOLOv5 vs. YOLOv8: Performance Benchmarking in Wildfire and Smoke Detection Scenarios

Edmundo Casas 1,2, Leo Ramos 1,3,4,*, Eduardo Bendek 1,5, and Francklin Rivas-Echeverria 1,6
1. Kauel Inc., Houston, USA
2. Faculty of Engineering, University of Deusto, Bilbao, Spain
3. Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain
4. School of Mathematical and Computational Sciences, Yachay Tech University, Urcuquí, Ecuador
5. Jet Propulsion Laboratory, NASA, Pasadena, USA
6. Pontificia Universidad Católica del Ecuador Sede Ibarra, Ibarra, Ecuador
Email: edmundo.casas@kauel.com (E.C.); leo.ramos@kauel.com (L.R.); eduardo.bendek@kauel.com (E.B.); francklin.rivas@kauel.com (F.R.-E.)
*Corresponding author

Manuscript received October 20, 2023; revised November 29, 2023; accepted December 6, 2023; published April 10, 2024.

Abstract—This paper provides a thorough analysis and comparison of the YOLOv5 and YOLOv8 models for wildfire and smoke detection, using the Foggia dataset for evaluation. The study examines the small (s), medium (m), and large (l) variants of each architecture and employs various metrics, including recall, precision, F1-Score, and mAP@50, to assess performance. Additional considerations such as training and inference times, along with the number of epochs required for optimal recall, are also evaluated to gauge the models’ real-world efficiency and effectiveness. Quantitatively, YOLOv5 models generally outperform YOLOv8, with the YOLOv5s variant achieving the highest scores across all metrics. However, visual assessments reveal that YOLOv8 models exhibit similar, and in some cases superior, capabilities, particularly in detecting dark and dense smoke. Training times favor YOLOv5 models, contributing to their efficiency, and their shorter inference times offer advantages for real-time applications. While the “best model” variants confirm YOLOv5’s numerical dominance, YOLOv8’s “best models” also display competitive performance. Future research will explore model evaluation on diverse datasets and hyperparameter optimization to further enhance performance, adaptability, and applicability in various real-world object detection scenarios.

Keywords—wildfire detection, smoke detection, computer vision, deep learning, artificial intelligence, YOLO

Cite: Edmundo Casas, Leo Ramos, Eduardo Bendek, and Francklin Rivas-Echeverria, "YOLOv5 vs. YOLOv8: Performance Benchmarking in Wildfire and Smoke Detection Scenarios," Journal of Image and Graphics, Vol. 12, No. 2, pp. 127-136, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.