Fire-PPYOLOE: An Efficient Forest Fire Detector for Real-Time Wild Forest Fire Monitoring

Abstract

Forest fire has the characteristics of sudden and destructive, which threatens safety of people’s life and property. Automatic detection and early warning of forest fire in the early stage is very important for protecting forest resources and reducing disaster losses. Unmanned forest fire monitoring is one popular way of forest fire automatic detection. However, the actual forest environment is complex and diverse, and the vision image is affected by various factors easily such as geographical location, seasons, cloudy weather, day and night, etc. In this paper, we propose a novel fire detection method called Fire-PPYOLOE. We design a new backbone and neck structure leveraging large kernel convolution to capture a large arrange area of reception field based on the existing fast and accurate object detection model PP-YOLOE. In addition, our model maintains the high-speed performance of the single-stage detection model and reduces model parameters by using CSPNet significantly. Extensive experiments are conducted to show the effectiveness of Fire-PPYOLOE from the views of detection accuracy and speed. The results show that our Fire-PPYOLOE is able to detect the smoke- and flame-like objects because it can learn features around the object to be detected. It can provide real-time forest fire prevention and early detection.