Early forest-fire detection using scanning polarization lidar

Swin-YOLOv5: Research and Application of Fire and Smoke Detection Algorithm Based on YOLOv5

Accurate monitoring of fire and smoke plays an irreplaceable role in preventing fires and safeguarding the safety of citizens' lives and property. The network structure of YOLOv5 is simple, but using convolution to extract features will lead to some problems such as limited receptive field, poor feature extraction ability, and insufficient feature integration. In view of the current defects of YOLOv5 target detection algorithm, a new algorithm model named Swin-YOLOv5 was proposed in this work. Swin transformation mechanism was introduced into YOLOv5 network, which enhanced the receptive field and feature extraction ability of the model without changing the depth of the model. In order to enrich the feature map splicing method of weighted Concat and enhance the feature fusion ability of model pairs, the feature splicing method of three output heads of feature fusion layer network was improved. The feature fusion module was further modified, and the weighted feature splicing method was introduced to improve the network feature fusion ability. Experiments showed that, compared with the original algorithm, the rising rate of mAP@0.5 (mean average precision, IoU=0.5) of the improved algorithm was 0.7%, the mAP@0.5:0.95 was increased by 4.5%, and the target detection speed with high accuracy was accelerated by 1.8 FPS (frames per second) under the same experimental dataset. The improved algorithm could more accurately detect the targets that were not detected or detected inaccurately by the original algorithm, which embodied the adaptability of real scene detection and had practical significance. This work provided an opportunity for the application of fire-smoke detection in forest and indoor scenes and also developed a feasible idea for feature extraction and fusion of YOLOv5.