Wildfire Smoke Detection Based on Swin Transformer

Abstract

Wildfires have devastating consequences for ecosystems and human lives, and it is crucial to detect wildfires accurately for reducing losses. Most researchers detected wildfire smoke by traditional smoke detection algorithms, deep learning algorithms, or a combination of both, but this still has a high rate of false positive. To enhance the accuracy of wildfire smoke detection, we present an advanced algorithm based on the Swin Transformer architecture, which improves remote dependence of high-order feature maps in the convolutional neural network, thus reducing the false positive rate of smoke detection by introducing the STB module to the GoogLeNet. We approach the task of wildfire smoke detection as a classification problem, where we divide the dataset of 18, 000 real wildfire smoke images into two distinct sets: a training set and a testing set. The experimental results support the efficacy and practicality of the proposed algorithm, exhibiting a remarkable smoke detection accuracy rate of 95% while successfully reducing the false alarm rate to as low as 4%. These findings underscore the algorithm’s capability to accurately identify wildfire smoke instances while minimizing erroneous detections.