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

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.