YOLOv5s-FSDA: Fire and Smoke Detection Algorithm Based on Improved YOLOv5s

Fire and smoke detection are critical to reducing fire losses. Edge devices with limited resources require a model that is more lightweight while still maintaining the detection accuracy necessary for fire and smoke detection tasks. To solve these problems, this paper proposes a fire and smoke detection algorithm based on YOLOvSs, named YOLOvSsFSDA. Firstly, we incorporate the Cross-scale Feature Fusion (CFF) structure, enhancing the recognition and representation capabilities of features across different scales. Secondly, to further reduce the model burden, we adopt Ghost Convolution (GhostConv) technology to reduce model parameter count and computational load. Thirdly, to enhance the model's overall grasp of fire and smoke areas, we integrate the Content-Aware ReAssembly of Features (CARAFE) upsampling method. In addition, we introduce the Similarity Intersection over Union (SIoU) loss function to reduce the positioning error between the prediction and actual boxes, ensuring that fire and smoke targets of different scales can be located more accurately. Experimental results show that the YOLOvSs-FSDA algorithm, while maintaining the high detection accuracy of YOLOvSs, reduces model complexity by 30% and computational costs by 18%.