Fire Safety Detection Based on CAGSA-YOLO Network

Abstract

The layout of a city is complex, and indoor spaces have thousands of aspects that make them susceptible to fire. If a fire breaks out, it is difficult to quell, so a fire in the city will cause great harm. However, the traditional fire detection algorithm has a low detection efficiency and high detection rate of small targets, and disasters have occurred during detection. Therefore, this paper proposes a fire safety detection algorithm based on CAGSA-YOLO and constructs a fire safety dataset to integrate common fire safety tools into fire detection, which has a preventive detection effect before a fire occurs. In the improved algorithm, the upsampling in the original YOLOv5 is replaced with the CARAFE module. By adjusting its internal Parameter contrast, the algorithm pays more attention to local regional information and obtains stronger feature maps. Secondly, a new scale detection layer is added to detect objects larger than 4 × 4. Furthermore, the sampling Ghost lightweight design replaces C3 with the C3Ghost module without reducing the mAP. Finally, a lighter SA mechanism is introduced to optimize visual information processing resources. Using the fire safety dataset, the precision, recall, and mAP of the improved model increase to 89.7%, 80.1%, and 85.1%, respectively. At the same time, the size of the improved model is reduced by 0.6 M to 13.8 M, and the Param is reduced from 7.1 M to 6.6 M.