An enhanced YOLOv8 for flame and smoke detection with dilated convolution and image dehazing

This paper introduces FSD-YOLOv8, an advanced YOLOv8 algorithm specifically adapted for the precise detection of flames and smoke in various complex environments. By integrating a preliminary image dehazing phase, our algorithm significantly enhances the accuracy of smoke detection by reducing atmospheric confusion. Furthermore, we introduce the application of dilated convolutions within the YOLOv8 framework, which aids in detecting intricate features associated with fire and smoke. We present comprehensive evaluations of our approach using a large, dedicated flame and smoke dataset. Our findings demonstrate that FSD-YOLOv8 exhibits superior performance in detecting flame and smoke compared to conventional methods, paving the way for further advancements in machine learning-based fire detection systems.