An Improved Forest Smoke Detection Model Based on YOLOv8

This study centers on leveraging smoke detection for preemptive forest smoke detection. Owing to the inherent ambiguity and uncertainty in smoke characteristics, existing smoke detection algorithms suffer from reduced detection accuracy, elevated false alarm rates, and occurrences of omissions. To resolve these issues, this paper employs an efficient YOLOv8 network and integrates three novel detection modules for enhancement. These modules comprise the edge feature enhancement module, designed to identify smoke ambiguity features, alongside the multi-feature extraction module and the global feature enhancement module, targeting the detection of smoke uncertainty features. These modifications improve the accuracy of smoke area identification while notably lowering the rate of false alarms and omission phenomenon occurrences. Meanwhile, a large forest smoke dataset is created in this paper, which includes not only smoke images with normal forest backgrounds but also a considerable quantity of smoke images with complex backgrounds to enhance the algorithm’s robustness. The proposed algorithm in this paper achieves an AP of 79.1%, 79.2%, and 93.8% for the self-made dataset, XJTU-RS, and USTC-RF, respectively. These results surpass those obtained by the current state-of-the-art target detection-based and neural network-based improved smoke detection algorithms.