Assessing the Effectiveness of YOLO Architectures for Smoke and Wildfire Detection

Abstract

This paper presents a comprehensive evaluation of various YOLO architectures for smoke and wildfire detection, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, and YOLO-NAS. The study aims to assess their effectiveness in early detection of wildfires using the Foggia dataset, comprising 8,974 images specifically designed for this purpose. Performance evaluation employs metrics such as Recall, Precision, F1-score, and mean Average Precision to provide a holistic assessment of the models' performance. The study follows a rigorous methodology involving fixed epochs, continuous performance tracking, and unbiased testing. Results show that YOLOv5, YOLOv7, and YOLOv8 exhibit a balanced performance across all metrics in both validation and testing. YOLOv6 performs slightly lower in recall during validation but achieves a good balance on testing. YOLO-NAS variants excel in recall, making them suitable for critical applications. However, precision performance is lower for YOLO-NAS models. Visual results demonstrate that the top-performing models accurately identify most instances in the test set. However, they struggle with distant scenes and poor lighting conditions, occasionally detecting false positives. In favorable conditions, the models perform well in identifying relevant instances. We conclude that no single model excels in all aspects of smoke and wildfire detection. The choice of model depends on specific application requirements, considering accuracy, recall, and inference time. This research contributes to the field of computer vision in smoke and wildfire detection, providing a foundation for improving detection systems and mitigating the impact of wildfires. Researchers can build upon these findings to propose modifications and enhance the effectiveness of wildfire detection systems.

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