A Lightweight Convolutional Neural Network Flame Detection Algorithm

Flame detection is a key technical link to realize intelligent forest fire prevention and control. However, the current fire detection methods generally have the problems of low detection rate, high false alarm rate, and poor real-time performance. In order to achieve rapid and accurate recognition of forest fires in natural environments, this paper proposes a lightweight convolutional neural network flame detection algorithm Yolo-Edge. MobileNetv3 has deep separable convolutional structure features, which can replace Yolov4's original CSPDarknet53 feature extraction backbone network, and can reduce the number of network layers and model size, so that it can adapt to the working environment of edge devices and multi-scale prediction. Feature fusion is carried out through the feature pyramid to improve the detection accuracy of small targets. Use 2059 flame images in different occlusion environments as a data set for training and testing, and use F1 value and AP value to evaluate the difference of each model. The test results show that the lightweight improved neural network model proposed in this paper has good recognition accuracy and speed, which significantly reduces the memory usage of the model and achieves a good lightweight effect.