Research on Image-based wildfire intelligent detection method

Abstract

Wildfire, also known as forest fire, is fire that usually occur in forests and are difficult to control. If it could be detected and suppressed at an early stage (mainly smoke and flames), it has important meaning for reducing the loss. With the attention of relevant researchers, wildfire detection technology has become more and more advanced, from traditional manual monitoring to traditional target detection to sensor detection and infrared detection, etc. The various detection methods involved still have problems such as slow detection speed, low accuracy, easy interference and high cost. In this paper, SSD, an advanced target detection method, was chosen from deep learning algorithms. Three independent SSD networks are built with VGG16, MobileNet v2, and EfficientNet b3 as the backbone. The experimental results show that the mAP (mean Average Precision) of VGG16-SSD is 95.34%, which is 4.76% higher than MobileNet v2-SSD and 4.53% higher than EfficientNet b3-SSD. Therefore, VGG16-SSD can effectively detect wildfires in the early stages.