A Real-Time Smoke Detection Model Based on YOLO-SMOKE Algorithm

Abstract

Most existing smoke detection methods have low detection accuracy in complicated scenes. We propose a YOLO-SMOKE model which shows strong robustness and high accuracy. We use mosaic data augmentation to avoid model overfitting which is caused by the simple background in the public datasets. We refine the residual module by using the efficient channel attention module and add dropblock after every convolutional layer. Then we modify the loss function. The YOLO-SMOKE model outperforms the original model by 4.91% mAP and it achieves 98.75% accuracy in the public smoke video test. The experimental results show that our method has better accurate and robustness.